In [1]:
!pip install -U seaborn
Requirement already up-to-date: seaborn in /opt/conda/lib/python3.6/site-packages (0.9.0) Requirement already satisfied, skipping upgrade: numpy>=1.9.3 in /opt/conda/lib/python3.6/site-packages (from seaborn) (1.12.1) Requirement already satisfied, skipping upgrade: pandas>=0.15.2 in /opt/conda/lib/python3.6/site-packages (from seaborn) (0.23.3) Requirement already satisfied, skipping upgrade: scipy>=0.14.0 in /opt/conda/lib/python3.6/site-packages (from seaborn) (0.19.1) Requirement already satisfied, skipping upgrade: matplotlib>=1.4.3 in /opt/conda/lib/python3.6/site-packages (from seaborn) (2.1.0) Requirement already satisfied, skipping upgrade: python-dateutil>=2.5.0 in /opt/conda/lib/python3.6/site-packages (from pandas>=0.15.2->seaborn) (2.6.1) Requirement already satisfied, skipping upgrade: pytz>=2011k in /opt/conda/lib/python3.6/site-packages (from pandas>=0.15.2->seaborn) (2017.3) Requirement already satisfied, skipping upgrade: six>=1.10 in /opt/conda/lib/python3.6/site-packages (from matplotlib>=1.4.3->seaborn) (1.11.0) Requirement already satisfied, skipping upgrade: cycler>=0.10 in /opt/conda/lib/python3.6/site-packages/cycler-0.10.0-py3.6.egg (from matplotlib>=1.4.3->seaborn) (0.10.0) Requirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /opt/conda/lib/python3.6/site-packages (from matplotlib>=1.4.3->seaborn) (2.2.0)
In [2]:
## Importing the necessary packages required for the project
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
import requests
import json
import tweepy
from timeit import default_timer as timer
import glob
import re
import seaborn as sns
Gathering¶
In [3]:
## Reading the twitter_ archive-enhnced csv file
df1=pd.read_csv("twitter-archive-enhanced.csv")
In [4]:
df1.head()
Out[4]:
| tweet_id | in_reply_to_status_id | in_reply_to_user_id | timestamp | source | text | retweeted_status_id | retweeted_status_user_id | retweeted_status_timestamp | expanded_urls | rating_numerator | rating_denominator | name | doggo | floofer | pupper | puppo | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 892420643555336193 | NaN | NaN | 2017-08-01 16:23:56 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Phineas. He's a mystical boy. Only eve... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/892420643... | 13 | 10 | Phineas | None | None | None | None |
| 1 | 892177421306343426 | NaN | NaN | 2017-08-01 00:17:27 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Tilly. She's just checking pup on you.... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/892177421... | 13 | 10 | Tilly | None | None | None | None |
| 2 | 891815181378084864 | NaN | NaN | 2017-07-31 00:18:03 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Archie. He is a rare Norwegian Pouncin... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/891815181... | 12 | 10 | Archie | None | None | None | None |
| 3 | 891689557279858688 | NaN | NaN | 2017-07-30 15:58:51 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Darla. She commenced a snooze mid meal... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/891689557... | 13 | 10 | Darla | None | None | None | None |
| 4 | 891327558926688256 | NaN | NaN | 2017-07-29 16:00:24 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Franklin. He would like you to stop ca... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/891327558... | 12 | 10 | Franklin | None | None | None | None |
In [5]:
## downloading the html filw with requests library
url = ' https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv'
response = requests.get(url)
In [6]:
## Saving the HTML to a file
with open ("image-predictions.tsv" , mode= 'wb') as file :
file.write (response.content)
In [7]:
## Reading the tsv file
imagedf1 = pd.read_csv('image-predictions.tsv',sep ='\t')
In [8]:
imagedf1.head()
Out[8]:
| tweet_id | jpg_url | img_num | p1 | p1_conf | p1_dog | p2 | p2_conf | p2_dog | p3 | p3_conf | p3_dog | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 666020888022790149 | https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg | 1 | Welsh_springer_spaniel | 0.465074 | True | collie | 0.156665 | True | Shetland_sheepdog | 0.061428 | True |
| 1 | 666029285002620928 | https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg | 1 | redbone | 0.506826 | True | miniature_pinscher | 0.074192 | True | Rhodesian_ridgeback | 0.072010 | True |
| 2 | 666033412701032449 | https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg | 1 | German_shepherd | 0.596461 | True | malinois | 0.138584 | True | bloodhound | 0.116197 | True |
| 3 | 666044226329800704 | https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg | 1 | Rhodesian_ridgeback | 0.408143 | True | redbone | 0.360687 | True | miniature_pinscher | 0.222752 | True |
| 4 | 666049248165822465 | https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg | 1 | miniature_pinscher | 0.560311 | True | Rottweiler | 0.243682 | True | Doberman | 0.154629 | True |
Query Twitter API for each tweet in the Twitter archive and save JSON in a text file¶
These are hidden to comply with Twitter's API terms and conditions¶
consumer_key = 'Your credentials' consumer_secret = 'Your credentials' access_token = 'Your Credentials' access_secret = 'Your Credentials'
auth = OAuthHandler(consumer_key, consumer_secret) auth.set_access_token(access_token, access_secret)
Twitter api code for accessing the data from the Twitter API¶
Twitter api code will extract information based upon the tweet_id given in the df1¶
df1.tweet_id.values creates an array of all the id values in tweet_id¶
api = tweepy.API(auth, wait_on_rate_limit=True) tweet_ids = df1.tweet_id.values len(tweet_ids)
Query Twitter's API for JSON data for each tweet ID in the Twitter archive¶
count = 0 fails_dict = {} start = timer()
Save each tweet's returned JSON as a new line in a .txt file¶
with open('tweet_json.txt', 'w') as outfile:
# This loop will likely take 20-30 minutes to run because of Twitter's rate limit
for tweet_id in tweet_ids:
count += 1
print(str(count) + ": " + str(tweet_id))
try:
tweet = api.get_status(tweet_id, tweet_mode='extended')
print("Success")
json.dump(tweet._json, outfile)
outfile.write('\n')
except tweepy.TweepError as e:
print("Fail")
fails_dict[tweet_id] = e
pass
end = timer() print(end - start) print(fails_dict)
In [9]:
## Loading the text file
## We are creating an empty list, and opening the the text file with json structure and reading line by line for extracting the data
tweet_api =[]
with open('tweet_json.txt','r') as f:
for line in f:
tweet_api.append(json.loads(line))
In [10]:
## Displaying the tweet_api list
tweet_api
Out[10]:
[{'created_at': 'Tue Aug 01 16:23:56 +0000 2017',
'id': 892420643555336193,
'id_str': '892420643555336193',
'full_text': "This is Phineas. He's a mystical boy. Only ever appears in the hole of a donut. 13/10 https://t.co/MgUWQ76dJU",
'truncated': False,
'display_text_range': [0, 85],
'entities': {'hashtags': [],
'symbols': [],
'user_mentions': [],
'urls': [],
'media': [{'id': 892420639486877696,
'id_str': '892420639486877696',
'indices': [86, 109],
'media_url': 'http://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg',
'media_url_https': 'https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg',
'url': 'https://t.co/MgUWQ76dJU',
'display_url': 'pic.twitter.com/MgUWQ76dJU',
'expanded_url': 'https://twitter.com/dog_rates/status/892420643555336193/photo/1',
'type': 'photo',
'sizes': {'large': {'w': 540, 'h': 528, 'resize': 'fit'},
'thumb': {'w': 150, 'h': 150, 'resize': 'crop'},
'small': {'w': 540, 'h': 528, 'resize': 'fit'},
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'url': 'https://t.co/MgUWQ76dJU',
'display_url': 'pic.twitter.com/MgUWQ76dJU',
'expanded_url': 'https://twitter.com/dog_rates/status/892420643555336193/photo/1',
'type': 'photo',
'sizes': {'large': {'w': 540, 'h': 528, 'resize': 'fit'},
'thumb': {'w': 150, 'h': 150, 'resize': 'crop'},
'small': {'w': 540, 'h': 528, 'resize': 'fit'},
'medium': {'w': 540, 'h': 528, 'resize': 'fit'}}}]},
'source': '<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>',
'in_reply_to_status_id': None,
'in_reply_to_status_id_str': None,
'in_reply_to_user_id': None,
'in_reply_to_user_id_str': None,
'in_reply_to_screen_name': None,
'user': {'id': 4196983835,
'id_str': '4196983835',
'name': 'WeRateDogs™ (author)',
'screen_name': 'dog_rates',
'location': 'DM YOUR DOGS, WE WILL RATE',
'description': '#1 Source for Professional Dog Ratings | STORE: @ShopWeRateDogs | IG, FB & SC: WeRateDogs MOBILE APP: @GoodDogsGame | Business: dogratingtwitter@gmail.com',
'url': 'https://t.co/N7sNNHAEXS',
'entities': {'url': {'urls': [{'url': 'https://t.co/N7sNNHAEXS',
'expanded_url': 'http://weratedogs.com',
'display_url': 'weratedogs.com',
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'description': {'urls': []}},
'protected': False,
'followers_count': 3200889,
'friends_count': 104,
'listed_count': 2784,
'created_at': 'Sun Nov 15 21:41:29 +0000 2015',
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'default_profile_image': False,
'following': True,
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'translator_type': 'none'},
'geo': None,
'coordinates': None,
'place': None,
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'is_quote_status': False,
'retweet_count': 8853,
'favorite_count': 39467,
'favorited': False,
'retweeted': False,
'possibly_sensitive': False,
'possibly_sensitive_appealable': False,
'lang': 'en'},
{'created_at': 'Tue Aug 01 00:17:27 +0000 2017',
'id': 892177421306343426,
'id_str': '892177421306343426',
'full_text': "This is Tilly. She's just checking pup on you. Hopes you're doing ok. If not, she's available for pats, snugs, boops, the whole bit. 13/10 https://t.co/0Xxu71qeIV",
'truncated': False,
'display_text_range': [0, 138],
'entities': {'hashtags': [],
'symbols': [],
'user_mentions': [],
'urls': [],
'media': [{'id': 892177413194625024,
'id_str': '892177413194625024',
'indices': [139, 162],
'media_url': 'http://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg',
'media_url_https': 'https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg',
'url': 'https://t.co/0Xxu71qeIV',
'display_url': 'pic.twitter.com/0Xxu71qeIV',
'expanded_url': 'https://twitter.com/dog_rates/status/892177421306343426/photo/1',
'type': 'photo',
'sizes': {'large': {'w': 1407, 'h': 1600, 'resize': 'fit'},
'thumb': {'w': 150, 'h': 150, 'resize': 'crop'},
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'extended_entities': {'media': [{'id': 892177413194625024,
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'display_url': 'pic.twitter.com/0Xxu71qeIV',
'expanded_url': 'https://twitter.com/dog_rates/status/892177421306343426/photo/1',
'type': 'photo',
'sizes': {'large': {'w': 1407, 'h': 1600, 'resize': 'fit'},
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'small': {'w': 598, 'h': 680, 'resize': 'fit'},
'medium': {'w': 1055, 'h': 1200, 'resize': 'fit'}}}]},
'source': '<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>',
'in_reply_to_status_id': None,
'in_reply_to_status_id_str': None,
'in_reply_to_user_id': None,
'in_reply_to_user_id_str': None,
'in_reply_to_screen_name': None,
'user': {'id': 4196983835,
'id_str': '4196983835',
'name': 'WeRateDogs™ (author)',
'screen_name': 'dog_rates',
'location': 'DM YOUR DOGS, WE WILL RATE',
'description': '#1 Source for Professional Dog Ratings | STORE: @ShopWeRateDogs | IG, FB & SC: WeRateDogs MOBILE APP: @GoodDogsGame | Business: dogratingtwitter@gmail.com',
'url': 'https://t.co/N7sNNHAEXS',
'entities': {'url': {'urls': [{'url': 'https://t.co/N7sNNHAEXS',
'expanded_url': 'http://weratedogs.com',
'display_url': 'weratedogs.com',
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'description': {'urls': []}},
'protected': False,
'followers_count': 3200889,
'friends_count': 104,
'listed_count': 2784,
'created_at': 'Sun Nov 15 21:41:29 +0000 2015',
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'utc_offset': None,
'time_zone': None,
'geo_enabled': True,
'verified': True,
'statuses_count': 5288,
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'default_profile': False,
'default_profile_image': False,
'following': True,
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'translator_type': 'none'},
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'retweet_count': 6514,
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'retweeted': False,
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{'created_at': 'Mon Jul 31 00:18:03 +0000 2017',
'id': 891815181378084864,
'id_str': '891815181378084864',
'full_text': 'This is Archie. He is a rare Norwegian Pouncing Corgo. Lives in the tall grass. You never know when one may strike. 12/10 https://t.co/wUnZnhtVJB',
'truncated': False,
'display_text_range': [0, 121],
'entities': {'hashtags': [],
'symbols': [],
'user_mentions': [],
'urls': [],
'media': [{'id': 891815175371796480,
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'indices': [122, 145],
'media_url': 'http://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg',
'media_url_https': 'https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg',
'url': 'https://t.co/wUnZnhtVJB',
'display_url': 'pic.twitter.com/wUnZnhtVJB',
'expanded_url': 'https://twitter.com/dog_rates/status/891815181378084864/photo/1',
'type': 'photo',
'sizes': {'medium': {'w': 901, 'h': 1200, 'resize': 'fit'},
'thumb': {'w': 150, 'h': 150, 'resize': 'crop'},
'large': {'w': 1201, 'h': 1600, 'resize': 'fit'},
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'url': 'https://t.co/wUnZnhtVJB',
'display_url': 'pic.twitter.com/wUnZnhtVJB',
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'sizes': {'medium': {'w': 901, 'h': 1200, 'resize': 'fit'},
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'source': '<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a>',
'in_reply_to_status_id': None,
'in_reply_to_status_id_str': None,
'in_reply_to_user_id': None,
'in_reply_to_user_id_str': None,
'in_reply_to_screen_name': None,
'user': {'id': 4196983835,
'id_str': '4196983835',
'name': 'WeRateDogs™ (author)',
'screen_name': 'dog_rates',
'location': 'DM YOUR DOGS, WE WILL RATE',
'description': '#1 Source for Professional Dog Ratings | STORE: @ShopWeRateDogs | IG, FB & SC: WeRateDogs MOBILE APP: @GoodDogsGame | Business: dogratingtwitter@gmail.com',
'url': 'https://t.co/N7sNNHAEXS',
'entities': {'url': {'urls': [{'url': 'https://t.co/N7sNNHAEXS',
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'description': {'urls': []}},
'protected': False,
'followers_count': 3200889,
'friends_count': 104,
'listed_count': 2784,
'created_at': 'Sun Nov 15 21:41:29 +0000 2015',
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'default_profile_image': False,
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'translator_type': 'none'},
'geo': None,
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'is_quote_status': False,
'retweet_count': 4328,
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'favorited': False,
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'possibly_sensitive_appealable': False,
'lang': 'en'},
{'created_at': 'Sun Jul 30 15:58:51 +0000 2017',
'id': 891689557279858688,
'id_str': '891689557279858688',
'full_text': 'This is Darla. She commenced a snooze mid meal. 13/10 happens to the best of us https://t.co/tD36da7qLQ',
'truncated': False,
'display_text_range': [0, 79],
'entities': {'hashtags': [],
'symbols': [],
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'urls': [],
'media': [{'id': 891689552724799489,
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'media_url_https': 'https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg',
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'display_url': 'pic.twitter.com/tD36da7qLQ',
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'contributors_enabled': False,
'is_translator': False,
'is_translation_enabled': False,
'profile_background_color': '000000',
'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png',
'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png',
'profile_background_tile': False,
'profile_image_url': 'http://pbs.twimg.com/profile_images/861415328504569856/R2xOOfwe_normal.jpg',
'profile_image_url_https': 'https://pbs.twimg.com/profile_images/861415328504569856/R2xOOfwe_normal.jpg',
'profile_banner_url': 'https://pbs.twimg.com/profile_banners/4196983835/1501129017',
'profile_link_color': 'F5ABB5',
'profile_sidebar_border_color': '000000',
'profile_sidebar_fill_color': '000000',
'profile_text_color': '000000',
'profile_use_background_image': False,
'has_extended_profile': True,
'default_profile': False,
'default_profile_image': False,
'following': True,
'follow_request_sent': False,
'notifications': False,
'translator_type': 'none'},
'geo': None,
'coordinates': None,
'place': None,
'contributors': None,
'is_quote_status': False,
'retweet_count': 2894,
'favorite_count': 7310,
'favorited': False,
'retweeted': False,
'possibly_sensitive': False,
'possibly_sensitive_appealable': False,
'lang': 'en'},
...]
In [11]:
##Converting the list into dataframe
tweets_api=pd.DataFrame(tweet_api)
In [12]:
tweets_api
Out[12]:
| contributors | coordinates | created_at | display_text_range | entities | extended_entities | favorite_count | favorited | full_text | geo | ... | possibly_sensitive_appealable | quoted_status | quoted_status_id | quoted_status_id_str | retweet_count | retweeted | retweeted_status | source | truncated | user | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | None | None | Tue Aug 01 16:23:56 +0000 2017 | [0, 85] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 892420639486877696, 'id_str'... | 39467 | False | This is Phineas. He's a mystical boy. Only eve... | None | ... | False | NaN | NaN | NaN | 8853 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 1 | None | None | Tue Aug 01 00:17:27 +0000 2017 | [0, 138] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 892177413194625024, 'id_str'... | 33819 | False | This is Tilly. She's just checking pup on you.... | None | ... | False | NaN | NaN | NaN | 6514 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2 | None | None | Mon Jul 31 00:18:03 +0000 2017 | [0, 121] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 891815175371796480, 'id_str'... | 25461 | False | This is Archie. He is a rare Norwegian Pouncin... | None | ... | False | NaN | NaN | NaN | 4328 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 3 | None | None | Sun Jul 30 15:58:51 +0000 2017 | [0, 79] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 891689552724799489, 'id_str'... | 42908 | False | This is Darla. She commenced a snooze mid meal... | None | ... | False | NaN | NaN | NaN | 8964 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 4 | None | None | Sat Jul 29 16:00:24 +0000 2017 | [0, 138] | {'hashtags': [{'text': 'BarkWeek', 'indices': ... | {'media': [{'id': 891327551943041024, 'id_str'... | 41048 | False | This is Franklin. He would like you to stop ca... | None | ... | False | NaN | NaN | NaN | 9774 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 5 | None | None | Sat Jul 29 00:08:17 +0000 2017 | [0, 138] | {'hashtags': [{'text': 'BarkWeek', 'indices': ... | {'media': [{'id': 891087942176911360, 'id_str'... | 20562 | False | Here we have a majestic great white breaching ... | None | ... | False | NaN | NaN | NaN | 3261 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 6 | None | None | Fri Jul 28 16:27:12 +0000 2017 | [0, 140] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 890971906207338496, 'id_str'... | 12041 | False | Meet Jax. He enjoys ice cream so much he gets ... | None | ... | False | NaN | NaN | NaN | 2158 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 7 | None | None | Fri Jul 28 00:22:40 +0000 2017 | [0, 118] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 890729118844600320, 'id_str'... | 56848 | False | When you watch your owner call another dog a g... | None | ... | False | NaN | NaN | NaN | 16716 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 8 | None | None | Thu Jul 27 16:25:51 +0000 2017 | [0, 122] | {'hashtags': [{'text': 'BarkWeek', 'indices': ... | {'media': [{'id': 890609177319665665, 'id_str'... | 28226 | False | This is Zoey. She doesn't want to be one of th... | None | ... | False | NaN | NaN | NaN | 4429 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 9 | None | None | Wed Jul 26 15:59:51 +0000 2017 | [0, 133] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 890240245463175168, 'id_str'... | 32467 | False | This is Cassie. She is a college pup. Studying... | None | ... | False | NaN | NaN | NaN | 7711 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 10 | None | None | Wed Jul 26 00:31:25 +0000 2017 | [0, 130] | {'hashtags': [{'text': 'BarkWeek', 'indices': ... | {'media': [{'id': 890006600089468928, 'id_str'... | 31166 | False | This is Koda. He is a South Australian decksha... | None | ... | False | NaN | NaN | NaN | 7624 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 11 | None | None | Tue Jul 25 16:11:53 +0000 2017 | [0, 107] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 889880888800096258, 'id_str'... | 28268 | False | This is Bruno. He is a service shark. Only get... | None | ... | False | NaN | NaN | NaN | 5156 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 12 | None | None | Tue Jul 25 01:55:32 +0000 2017 | [0, 106] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 889665366129029120, 'id_str'... | 38818 | False | Here's a puppo that seems to be on the fence a... | None | ... | False | NaN | NaN | NaN | 8538 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 13 | None | None | Tue Jul 25 00:10:02 +0000 2017 | [0, 91] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 889638825424826374, 'id_str'... | 27672 | False | This is Ted. He does his best. Sometimes that'... | None | ... | False | NaN | NaN | NaN | 4735 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 14 | None | None | Mon Jul 24 17:02:04 +0000 2017 | [0, 118] | {'hashtags': [{'text': 'BarkWeek', 'indices': ... | {'media': [{'id': 889531127467266049, 'id_str'... | 15359 | False | This is Stuart. He's sporting his favorite fan... | None | ... | False | NaN | NaN | NaN | 2321 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 15 | None | None | Mon Jul 24 00:19:32 +0000 2017 | [0, 138] | {'hashtags': [{'text': 'BarkWeek', 'indices': ... | {'media': [{'id': 889278779352338437, 'id_str'... | 25652 | False | This is Oliver. You're witnessing one of his m... | None | ... | False | NaN | NaN | NaN | 5637 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 16 | None | None | Sun Jul 23 00:22:39 +0000 2017 | [0, 86] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 888917229776945152, 'id_str'... | 29611 | False | This is Jim. He found a fren. Taught him how t... | None | ... | False | NaN | NaN | NaN | 4709 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 17 | None | None | Sat Jul 22 16:56:37 +0000 2017 | [0, 128] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 888804981515575296, 'id_str'... | 26080 | False | This is Zeke. He has a new stick. Very proud o... | None | ... | False | NaN | NaN | NaN | 4559 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 18 | None | None | Sat Jul 22 00:23:06 +0000 2017 | [0, 87] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 888554915546542081, 'id_str'... | 20290 | False | This is Ralphus. He's powering up. Attempting ... | None | ... | False | NaN | NaN | NaN | 3732 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 19 | None | None | Thu Jul 20 16:49:33 +0000 2017 | [0, 127] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 888078426338406400, 'id_str'... | 22201 | False | This is Gerald. He was just told he didn't get... | None | ... | False | NaN | NaN | NaN | 3653 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 20 | None | None | Wed Jul 19 16:06:48 +0000 2017 | [0, 127] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 887705281597243393, 'id_str'... | 30779 | False | This is Jeffrey. He has a monopoly on the pool... | None | ... | False | NaN | NaN | NaN | 5609 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 21 | None | None | Wed Jul 19 03:39:09 +0000 2017 | [0, 108] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 887517108413886465, 'id_str'... | 46959 | True | I've yet to rate a Venezuelan Hover Wiener. Th... | None | ... | False | NaN | NaN | NaN | 12082 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 22 | None | None | Wed Jul 19 00:47:34 +0000 2017 | [0, 99] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 887473949361045505, 'id_str'... | 69871 | False | This is Canela. She attempted some fancy porch... | None | ... | False | NaN | NaN | NaN | 18781 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 23 | None | None | Tue Jul 18 16:08:03 +0000 2017 | [0, 88] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 887343120832229379, 'id_str'... | 34222 | False | You may not have known you needed to see this ... | None | ... | False | NaN | NaN | NaN | 10737 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 24 | None | None | Tue Jul 18 00:07:08 +0000 2017 | [0, 129] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 887101385971384320, 'id_str'... | 31061 | False | This... is a Jubilant Antarctic House Bear. We... | None | ... | False | NaN | NaN | NaN | 6167 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 25 | None | None | Mon Jul 17 16:17:36 +0000 2017 | [0, 101] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 886983218871902208, 'id_str'... | 35859 | False | This is Maya. She's very shy. Rarely leaves he... | None | ... | False | NaN | NaN | NaN | 8084 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 26 | None | None | Sun Jul 16 23:58:41 +0000 2017 | [0, 121] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 886736868116754432, 'id_str'... | 12306 | False | This is Mingus. He's a wonderful father to his... | None | ... | False | NaN | NaN | NaN | 3443 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 27 | None | None | Sun Jul 16 20:14:00 +0000 2017 | [0, 71] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 886680331239161856, 'id_str'... | 22798 | False | This is Derek. He's late for a dog meeting. 13... | None | ... | False | NaN | NaN | NaN | 4610 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 28 | None | None | Sat Jul 15 23:25:31 +0000 2017 | [0, 131] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 886366138128449536, 'id_str'... | 21524 | False | This is Roscoe. Another pupper fallen victim t... | None | ... | False | NaN | NaN | NaN | 3316 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 29 | None | None | Sat Jul 15 16:51:35 +0000 2017 | [27, 105] | {'hashtags': [], 'symbols': [], 'user_mentions... | NaN | 117 | False | @NonWhiteHat @MayhewMayhem omg hello tanner yo... | None | ... | NaN | NaN | NaN | NaN | 4 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2324 | None | None | Tue Nov 17 00:53:15 +0000 2015 | [0, 138] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666418781095260160, 'id_str'... | 129 | False | This is Walter. He is an Alaskan Terrapin. Lov... | None | ... | False | NaN | NaN | NaN | 48 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2325 | None | None | Tue Nov 17 00:24:19 +0000 2015 | [0, 140] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666411498068123649, 'id_str'... | 459 | False | This is quite the dog. Gets really excited whe... | None | ... | False | NaN | NaN | NaN | 339 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2326 | None | None | Mon Nov 16 23:23:41 +0000 2015 | [0, 137] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666396240351993856, 'id_str'... | 172 | False | Oh goodness. A super rare northeast Qdoba kang... | None | ... | False | NaN | NaN | NaN | 92 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2327 | None | None | Mon Nov 16 21:54:18 +0000 2015 | [0, 81] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666373746337402880, 'id_str'... | 194 | False | Those are sunglasses and a jean jacket. 11/10 ... | None | ... | False | NaN | NaN | NaN | 100 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2328 | None | None | Mon Nov 16 21:10:36 +0000 2015 | [0, 140] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666362717482020864, 'id_str'... | 804 | False | Unique dog here. Very small. Lives in containe... | None | ... | False | NaN | NaN | NaN | 595 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2329 | None | None | Mon Nov 16 20:32:58 +0000 2015 | [0, 135] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666353280906170368, 'id_str'... | 229 | False | Here we have a mixed Asiago from the Galápagos... | None | ... | False | NaN | NaN | NaN | 77 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2330 | None | None | Mon Nov 16 20:01:42 +0000 2015 | [0, 112] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666345414279471104, 'id_str'... | 307 | False | Look at this jokester thinking seat belt laws ... | None | ... | False | NaN | NaN | NaN | 146 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2331 | None | None | Mon Nov 16 19:31:45 +0000 2015 | [0, 139] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666337857791987715, 'id_str'... | 204 | False | This is an extremely rare horned Parthenon. No... | None | ... | False | NaN | NaN | NaN | 96 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2332 | None | None | Mon Nov 16 16:37:02 +0000 2015 | [0, 138] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666293909010702337, 'id_str'... | 522 | False | This is a funny dog. Weird toes. Won't come do... | None | ... | False | NaN | NaN | NaN | 368 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2333 | None | None | Mon Nov 16 16:11:11 +0000 2015 | [0, 136] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666287399580733440, 'id_str'... | 152 | False | This is an Albanian 3 1/2 legged Episcopalian... | None | ... | False | NaN | NaN | NaN | 71 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2334 | None | None | Mon Nov 16 15:14:19 +0000 2015 | [0, 46] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666273081518768128, 'id_str'... | 184 | False | Can take selfies 11/10 https://t.co/ws2AMaNwPW | None | ... | False | NaN | NaN | NaN | 82 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2335 | None | None | Mon Nov 16 14:57:41 +0000 2015 | [0, 82] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666268904428277760, 'id_str'... | 108 | False | Very concerned about fellow dog trapped in com... | None | ... | False | NaN | NaN | NaN | 37 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2336 | None | None | Mon Nov 16 04:02:55 +0000 2015 | [0, 134] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666104129232740352, 'id_str'... | 14765 | False | Not familiar with this breed. No tail (weird).... | None | ... | False | NaN | NaN | NaN | 6871 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2337 | None | None | Mon Nov 16 03:55:04 +0000 2015 | [0, 128] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666102150364286977, 'id_str'... | 81 | False | Oh my. Here you are seeing an Adobe Setter giv... | None | ... | False | NaN | NaN | NaN | 16 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2338 | None | None | Mon Nov 16 03:44:34 +0000 2015 | [0, 140] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666099505364733952, 'id_str'... | 164 | False | Can stand on stump for what seems like a while... | None | ... | False | NaN | NaN | NaN | 73 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2339 | None | None | Mon Nov 16 03:22:39 +0000 2015 | [0, 132] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666093996847063040, 'id_str'... | 169 | False | This appears to be a Mongolian Presbyterian mi... | None | ... | False | NaN | NaN | NaN | 79 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2340 | None | None | Mon Nov 16 02:38:37 +0000 2015 | [0, 125] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666082912819875840, 'id_str'... | 121 | False | Here we have a well-established sunblockerspan... | None | ... | False | NaN | NaN | NaN | 47 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2341 | None | None | Mon Nov 16 01:59:36 +0000 2015 | [0, 137] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666073098362486784, 'id_str'... | 335 | False | Let's hope this flight isn't Malaysian (lol). ... | None | ... | False | NaN | NaN | NaN | 174 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2342 | None | None | Mon Nov 16 01:52:02 +0000 2015 | [0, 137] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666071190449033216, 'id_str'... | 154 | False | Here we have a northern speckled Rhododendron.... | None | ... | False | NaN | NaN | NaN | 67 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2343 | None | None | Mon Nov 16 01:22:45 +0000 2015 | [0, 107] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666063820255862784, 'id_str'... | 496 | False | This is the happiest dog you will ever see. Ve... | None | ... | False | NaN | NaN | NaN | 232 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2344 | None | None | Mon Nov 16 01:01:59 +0000 2015 | [0, 135] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666058597072306176, 'id_str'... | 115 | False | Here is the Rand Paul of retrievers folks! He'... | None | ... | False | NaN | NaN | NaN | 61 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2345 | None | None | Mon Nov 16 00:55:59 +0000 2015 | [0, 124] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666057085227016192, 'id_str'... | 304 | False | My oh my. This is a rare blond Canadian terrie... | None | ... | False | NaN | NaN | NaN | 146 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2346 | None | None | Mon Nov 16 00:49:46 +0000 2015 | [0, 140] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666055517517848576, 'id_str'... | 448 | False | Here is a Siberian heavily armored polar bear ... | None | ... | False | NaN | NaN | NaN | 261 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2347 | None | None | Mon Nov 16 00:35:11 +0000 2015 | [0, 138] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666051848592334848, 'id_str'... | 1253 | False | This is an odd dog. Hard on the outside but lo... | None | ... | False | NaN | NaN | NaN | 879 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2348 | None | None | Mon Nov 16 00:30:50 +0000 2015 | [0, 140] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666050754986266625, 'id_str'... | 136 | False | This is a truly beautiful English Wilson Staff... | None | ... | False | NaN | NaN | NaN | 60 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2349 | None | None | Mon Nov 16 00:24:50 +0000 2015 | [0, 120] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666049244999131136, 'id_str'... | 111 | False | Here we have a 1949 1st generation vulpix. Enj... | None | ... | False | NaN | NaN | NaN | 41 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2350 | None | None | Mon Nov 16 00:04:52 +0000 2015 | [0, 137] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666044217047650304, 'id_str'... | 311 | False | This is a purebred Piers Morgan. Loves to Netf... | None | ... | False | NaN | NaN | NaN | 147 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2351 | None | None | Sun Nov 15 23:21:54 +0000 2015 | [0, 130] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666033409081393153, 'id_str'... | 128 | False | Here is a very happy pup. Big fan of well-main... | None | ... | False | NaN | NaN | NaN | 47 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2352 | None | None | Sun Nov 15 23:05:30 +0000 2015 | [0, 139] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666029276303482880, 'id_str'... | 132 | False | This is a western brown Mitsubishi terrier. Up... | None | ... | False | NaN | NaN | NaN | 48 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
| 2353 | None | None | Sun Nov 15 22:32:08 +0000 2015 | [0, 131] | {'hashtags': [], 'symbols': [], 'user_mentions... | {'media': [{'id': 666020881337073664, 'id_str'... | 2535 | False | Here we have a Japanese Irish Setter. Lost eye... | None | ... | False | NaN | NaN | NaN | 532 | False | NaN | <a href="http://twitter.com/download/iphone" r... | False | {'id': 4196983835, 'id_str': '4196983835', 'na... |
2354 rows × 31 columns
In [13]:
## Selecting only the columns required for analysis
tweets_api=tweets_api.loc[:,['id','favorite_count','retweet_count']]
In [14]:
## Displaying the dataframe
tweets_api.head()
Out[14]:
| id | favorite_count | retweet_count | |
|---|---|---|---|
| 0 | 892420643555336193 | 39467 | 8853 |
| 1 | 892177421306343426 | 33819 | 6514 |
| 2 | 891815181378084864 | 25461 | 4328 |
| 3 | 891689557279858688 | 42908 | 8964 |
| 4 | 891327558926688256 | 41048 | 9774 |
Assessing¶
In [15]:
df1
Out[15]:
| tweet_id | in_reply_to_status_id | in_reply_to_user_id | timestamp | source | text | retweeted_status_id | retweeted_status_user_id | retweeted_status_timestamp | expanded_urls | rating_numerator | rating_denominator | name | doggo | floofer | pupper | puppo | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 892420643555336193 | NaN | NaN | 2017-08-01 16:23:56 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Phineas. He's a mystical boy. Only eve... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/892420643... | 13 | 10 | Phineas | None | None | None | None |
| 1 | 892177421306343426 | NaN | NaN | 2017-08-01 00:17:27 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Tilly. She's just checking pup on you.... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/892177421... | 13 | 10 | Tilly | None | None | None | None |
| 2 | 891815181378084864 | NaN | NaN | 2017-07-31 00:18:03 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Archie. He is a rare Norwegian Pouncin... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/891815181... | 12 | 10 | Archie | None | None | None | None |
| 3 | 891689557279858688 | NaN | NaN | 2017-07-30 15:58:51 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Darla. She commenced a snooze mid meal... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/891689557... | 13 | 10 | Darla | None | None | None | None |
| 4 | 891327558926688256 | NaN | NaN | 2017-07-29 16:00:24 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Franklin. He would like you to stop ca... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/891327558... | 12 | 10 | Franklin | None | None | None | None |
| 5 | 891087950875897856 | NaN | NaN | 2017-07-29 00:08:17 +0000 | <a href="http://twitter.com/download/iphone" r... | Here we have a majestic great white breaching ... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/891087950... | 13 | 10 | None | None | None | None | None |
| 6 | 890971913173991426 | NaN | NaN | 2017-07-28 16:27:12 +0000 | <a href="http://twitter.com/download/iphone" r... | Meet Jax. He enjoys ice cream so much he gets ... | NaN | NaN | NaN | https://gofundme.com/ydvmve-surgery-for-jax,ht... | 13 | 10 | Jax | None | None | None | None |
| 7 | 890729181411237888 | NaN | NaN | 2017-07-28 00:22:40 +0000 | <a href="http://twitter.com/download/iphone" r... | When you watch your owner call another dog a g... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/890729181... | 13 | 10 | None | None | None | None | None |
| 8 | 890609185150312448 | NaN | NaN | 2017-07-27 16:25:51 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Zoey. She doesn't want to be one of th... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/890609185... | 13 | 10 | Zoey | None | None | None | None |
| 9 | 890240255349198849 | NaN | NaN | 2017-07-26 15:59:51 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Cassie. She is a college pup. Studying... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/890240255... | 14 | 10 | Cassie | doggo | None | None | None |
| 10 | 890006608113172480 | NaN | NaN | 2017-07-26 00:31:25 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Koda. He is a South Australian decksha... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/890006608... | 13 | 10 | Koda | None | None | None | None |
| 11 | 889880896479866881 | NaN | NaN | 2017-07-25 16:11:53 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Bruno. He is a service shark. Only get... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/889880896... | 13 | 10 | Bruno | None | None | None | None |
| 12 | 889665388333682689 | NaN | NaN | 2017-07-25 01:55:32 +0000 | <a href="http://twitter.com/download/iphone" r... | Here's a puppo that seems to be on the fence a... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/889665388... | 13 | 10 | None | None | None | None | puppo |
| 13 | 889638837579907072 | NaN | NaN | 2017-07-25 00:10:02 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Ted. He does his best. Sometimes that'... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/889638837... | 12 | 10 | Ted | None | None | None | None |
| 14 | 889531135344209921 | NaN | NaN | 2017-07-24 17:02:04 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Stuart. He's sporting his favorite fan... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/889531135... | 13 | 10 | Stuart | None | None | None | puppo |
| 15 | 889278841981685760 | NaN | NaN | 2017-07-24 00:19:32 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Oliver. You're witnessing one of his m... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/889278841... | 13 | 10 | Oliver | None | None | None | None |
| 16 | 888917238123831296 | NaN | NaN | 2017-07-23 00:22:39 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Jim. He found a fren. Taught him how t... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/888917238... | 12 | 10 | Jim | None | None | None | None |
| 17 | 888804989199671297 | NaN | NaN | 2017-07-22 16:56:37 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Zeke. He has a new stick. Very proud o... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/888804989... | 13 | 10 | Zeke | None | None | None | None |
| 18 | 888554962724278272 | NaN | NaN | 2017-07-22 00:23:06 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Ralphus. He's powering up. Attempting ... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/888554962... | 13 | 10 | Ralphus | None | None | None | None |
| 19 | 888202515573088257 | NaN | NaN | 2017-07-21 01:02:36 +0000 | <a href="http://twitter.com/download/iphone" r... | RT @dog_rates: This is Canela. She attempted s... | 8.874740e+17 | 4.196984e+09 | 2017-07-19 00:47:34 +0000 | https://twitter.com/dog_rates/status/887473957... | 13 | 10 | Canela | None | None | None | None |
| 20 | 888078434458587136 | NaN | NaN | 2017-07-20 16:49:33 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Gerald. He was just told he didn't get... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/888078434... | 12 | 10 | Gerald | None | None | None | None |
| 21 | 887705289381826560 | NaN | NaN | 2017-07-19 16:06:48 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Jeffrey. He has a monopoly on the pool... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/887705289... | 13 | 10 | Jeffrey | None | None | None | None |
| 22 | 887517139158093824 | NaN | NaN | 2017-07-19 03:39:09 +0000 | <a href="http://twitter.com/download/iphone" r... | I've yet to rate a Venezuelan Hover Wiener. Th... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/887517139... | 14 | 10 | such | None | None | None | None |
| 23 | 887473957103951883 | NaN | NaN | 2017-07-19 00:47:34 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Canela. She attempted some fancy porch... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/887473957... | 13 | 10 | Canela | None | None | None | None |
| 24 | 887343217045368832 | NaN | NaN | 2017-07-18 16:08:03 +0000 | <a href="http://twitter.com/download/iphone" r... | You may not have known you needed to see this ... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/887343217... | 13 | 10 | None | None | None | None | None |
| 25 | 887101392804085760 | NaN | NaN | 2017-07-18 00:07:08 +0000 | <a href="http://twitter.com/download/iphone" r... | This... is a Jubilant Antarctic House Bear. We... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/887101392... | 12 | 10 | None | None | None | None | None |
| 26 | 886983233522544640 | NaN | NaN | 2017-07-17 16:17:36 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Maya. She's very shy. Rarely leaves he... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/886983233... | 13 | 10 | Maya | None | None | None | None |
| 27 | 886736880519319552 | NaN | NaN | 2017-07-16 23:58:41 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Mingus. He's a wonderful father to his... | NaN | NaN | NaN | https://www.gofundme.com/mingusneedsus,https:/... | 13 | 10 | Mingus | None | None | None | None |
| 28 | 886680336477933568 | NaN | NaN | 2017-07-16 20:14:00 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Derek. He's late for a dog meeting. 13... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/886680336... | 13 | 10 | Derek | None | None | None | None |
| 29 | 886366144734445568 | NaN | NaN | 2017-07-15 23:25:31 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Roscoe. Another pupper fallen victim t... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/886366144... | 12 | 10 | Roscoe | None | None | pupper | None |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2326 | 666411507551481857 | NaN | NaN | 2015-11-17 00:24:19 +0000 | <a href="http://twitter.com/download/iphone" r... | This is quite the dog. Gets really excited whe... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666411507... | 2 | 10 | quite | None | None | None | None |
| 2327 | 666407126856765440 | NaN | NaN | 2015-11-17 00:06:54 +0000 | <a href="http://twitter.com/download/iphone" r... | This is a southern Vesuvius bumblegruff. Can d... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666407126... | 7 | 10 | a | None | None | None | None |
| 2328 | 666396247373291520 | NaN | NaN | 2015-11-16 23:23:41 +0000 | <a href="http://twitter.com/download/iphone" r... | Oh goodness. A super rare northeast Qdoba kang... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666396247... | 9 | 10 | None | None | None | None | None |
| 2329 | 666373753744588802 | NaN | NaN | 2015-11-16 21:54:18 +0000 | <a href="http://twitter.com/download/iphone" r... | Those are sunglasses and a jean jacket. 11/10 ... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666373753... | 11 | 10 | None | None | None | None | None |
| 2330 | 666362758909284353 | NaN | NaN | 2015-11-16 21:10:36 +0000 | <a href="http://twitter.com/download/iphone" r... | Unique dog here. Very small. Lives in containe... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666362758... | 6 | 10 | None | None | None | None | None |
| 2331 | 666353288456101888 | NaN | NaN | 2015-11-16 20:32:58 +0000 | <a href="http://twitter.com/download/iphone" r... | Here we have a mixed Asiago from the Galápagos... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666353288... | 8 | 10 | None | None | None | None | None |
| 2332 | 666345417576210432 | NaN | NaN | 2015-11-16 20:01:42 +0000 | <a href="http://twitter.com/download/iphone" r... | Look at this jokester thinking seat belt laws ... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666345417... | 10 | 10 | None | None | None | None | None |
| 2333 | 666337882303524864 | NaN | NaN | 2015-11-16 19:31:45 +0000 | <a href="http://twitter.com/download/iphone" r... | This is an extremely rare horned Parthenon. No... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666337882... | 9 | 10 | an | None | None | None | None |
| 2334 | 666293911632134144 | NaN | NaN | 2015-11-16 16:37:02 +0000 | <a href="http://twitter.com/download/iphone" r... | This is a funny dog. Weird toes. Won't come do... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666293911... | 3 | 10 | a | None | None | None | None |
| 2335 | 666287406224695296 | NaN | NaN | 2015-11-16 16:11:11 +0000 | <a href="http://twitter.com/download/iphone" r... | This is an Albanian 3 1/2 legged Episcopalian... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666287406... | 1 | 2 | an | None | None | None | None |
| 2336 | 666273097616637952 | NaN | NaN | 2015-11-16 15:14:19 +0000 | <a href="http://twitter.com/download/iphone" r... | Can take selfies 11/10 https://t.co/ws2AMaNwPW | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666273097... | 11 | 10 | None | None | None | None | None |
| 2337 | 666268910803644416 | NaN | NaN | 2015-11-16 14:57:41 +0000 | <a href="http://twitter.com/download/iphone" r... | Very concerned about fellow dog trapped in com... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666268910... | 10 | 10 | None | None | None | None | None |
| 2338 | 666104133288665088 | NaN | NaN | 2015-11-16 04:02:55 +0000 | <a href="http://twitter.com/download/iphone" r... | Not familiar with this breed. No tail (weird).... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666104133... | 1 | 10 | None | None | None | None | None |
| 2339 | 666102155909144576 | NaN | NaN | 2015-11-16 03:55:04 +0000 | <a href="http://twitter.com/download/iphone" r... | Oh my. Here you are seeing an Adobe Setter giv... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666102155... | 11 | 10 | None | None | None | None | None |
| 2340 | 666099513787052032 | NaN | NaN | 2015-11-16 03:44:34 +0000 | <a href="http://twitter.com/download/iphone" r... | Can stand on stump for what seems like a while... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666099513... | 8 | 10 | None | None | None | None | None |
| 2341 | 666094000022159362 | NaN | NaN | 2015-11-16 03:22:39 +0000 | <a href="http://twitter.com/download/iphone" r... | This appears to be a Mongolian Presbyterian mi... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666094000... | 9 | 10 | None | None | None | None | None |
| 2342 | 666082916733198337 | NaN | NaN | 2015-11-16 02:38:37 +0000 | <a href="http://twitter.com/download/iphone" r... | Here we have a well-established sunblockerspan... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666082916... | 6 | 10 | None | None | None | None | None |
| 2343 | 666073100786774016 | NaN | NaN | 2015-11-16 01:59:36 +0000 | <a href="http://twitter.com/download/iphone" r... | Let's hope this flight isn't Malaysian (lol). ... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666073100... | 10 | 10 | None | None | None | None | None |
| 2344 | 666071193221509120 | NaN | NaN | 2015-11-16 01:52:02 +0000 | <a href="http://twitter.com/download/iphone" r... | Here we have a northern speckled Rhododendron.... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666071193... | 9 | 10 | None | None | None | None | None |
| 2345 | 666063827256086533 | NaN | NaN | 2015-11-16 01:22:45 +0000 | <a href="http://twitter.com/download/iphone" r... | This is the happiest dog you will ever see. Ve... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666063827... | 10 | 10 | the | None | None | None | None |
| 2346 | 666058600524156928 | NaN | NaN | 2015-11-16 01:01:59 +0000 | <a href="http://twitter.com/download/iphone" r... | Here is the Rand Paul of retrievers folks! He'... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666058600... | 8 | 10 | the | None | None | None | None |
| 2347 | 666057090499244032 | NaN | NaN | 2015-11-16 00:55:59 +0000 | <a href="http://twitter.com/download/iphone" r... | My oh my. This is a rare blond Canadian terrie... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666057090... | 9 | 10 | a | None | None | None | None |
| 2348 | 666055525042405380 | NaN | NaN | 2015-11-16 00:49:46 +0000 | <a href="http://twitter.com/download/iphone" r... | Here is a Siberian heavily armored polar bear ... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666055525... | 10 | 10 | a | None | None | None | None |
| 2349 | 666051853826850816 | NaN | NaN | 2015-11-16 00:35:11 +0000 | <a href="http://twitter.com/download/iphone" r... | This is an odd dog. Hard on the outside but lo... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666051853... | 2 | 10 | an | None | None | None | None |
| 2350 | 666050758794694657 | NaN | NaN | 2015-11-16 00:30:50 +0000 | <a href="http://twitter.com/download/iphone" r... | This is a truly beautiful English Wilson Staff... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666050758... | 10 | 10 | a | None | None | None | None |
| 2351 | 666049248165822465 | NaN | NaN | 2015-11-16 00:24:50 +0000 | <a href="http://twitter.com/download/iphone" r... | Here we have a 1949 1st generation vulpix. Enj... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666049248... | 5 | 10 | None | None | None | None | None |
| 2352 | 666044226329800704 | NaN | NaN | 2015-11-16 00:04:52 +0000 | <a href="http://twitter.com/download/iphone" r... | This is a purebred Piers Morgan. Loves to Netf... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666044226... | 6 | 10 | a | None | None | None | None |
| 2353 | 666033412701032449 | NaN | NaN | 2015-11-15 23:21:54 +0000 | <a href="http://twitter.com/download/iphone" r... | Here is a very happy pup. Big fan of well-main... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666033412... | 9 | 10 | a | None | None | None | None |
| 2354 | 666029285002620928 | NaN | NaN | 2015-11-15 23:05:30 +0000 | <a href="http://twitter.com/download/iphone" r... | This is a western brown Mitsubishi terrier. Up... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666029285... | 7 | 10 | a | None | None | None | None |
| 2355 | 666020888022790149 | NaN | NaN | 2015-11-15 22:32:08 +0000 | <a href="http://twitter.com/download/iphone" r... | Here we have a Japanese Irish Setter. Lost eye... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/666020888... | 8 | 10 | None | None | None | None | None |
2356 rows × 17 columns
In [16]:
##df1 we can see the completeness issues
df1.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 2356 entries, 0 to 2355 Data columns (total 17 columns): tweet_id 2356 non-null int64 in_reply_to_status_id 78 non-null float64 in_reply_to_user_id 78 non-null float64 timestamp 2356 non-null object source 2356 non-null object text 2356 non-null object retweeted_status_id 181 non-null float64 retweeted_status_user_id 181 non-null float64 retweeted_status_timestamp 181 non-null object expanded_urls 2297 non-null object rating_numerator 2356 non-null int64 rating_denominator 2356 non-null int64 name 2356 non-null object doggo 2356 non-null object floofer 2356 non-null object pupper 2356 non-null object puppo 2356 non-null object dtypes: float64(4), int64(3), object(10) memory usage: 313.0+ KB
We can see that there are lot of null items tin in_reply_to_status_id,in_reply_to_user_id, retweeted_status_user_id,retweeted_status_user_id,retweeted_status_timestamp and there are some missing values in expanded_urls.Sometimes we can correct the compelteness issues but getting together with the stakeholders and soemtimes we have to leave it behind because we don't have any data source to extract data. But in this case we won't be using these columns so we will be removing these columns from the table
In [17]:
sum(df1['retweeted_status_id'].notnull())
Out[17]:
181
Remove retweeted_status_id (181 columns) as we are only interested in original tweet
In [18]:
type(df1['timestamp'][0])
Out[18]:
str
In [19]:
type(df1['tweet_id'][0])
Out[19]:
numpy.int64
Time in df1 dataframe is string we need to convert time into timestamp for anlysis, tweet_id should be a string
In [20]:
df1['tweet_id'].value_counts()
Out[20]:
749075273010798592 1
741099773336379392 1
798644042770751489 1
825120256414846976 1
769212283578875904 1
700462010979500032 1
780858289093574656 1
699775878809702401 1
880095782870896641 1
760521673607086080 1
776477788987613185 1
691820333922455552 1
715696743237730304 1
714606013974974464 1
760539183865880579 1
813157409116065792 1
676430933382295552 1
743510151680958465 1
837012587749474308 1
833722901757046785 1
818259473185828864 1
670704688707301377 1
667160273090932737 1
674394782723014656 1
672082170312290304 1
670093938074779648 1
759923798737051648 1
809920764300447744 1
805487436403003392 1
838085839343206401 1
..
763956972077010945 1
870308999962521604 1
720775346191278080 1
785927819176054784 1
783347506784731136 1
775733305207554048 1
834209720923721728 1
825026590719483904 1
758405701903519748 1
668986018524233728 1
690938899477221376 1
667911425562669056 1
754482103782404096 1
713175907180089344 1
669015743032369152 1
672068090318987265 1
816829038950027264 1
683773439333797890 1
674291837063053312 1
837482249356513284 1
767500508068192258 1
773922284943896577 1
673342308415348736 1
886054160059072513 1
748307329658011649 1
715360349751484417 1
666817836334096384 1
794926597468000259 1
673705679337693185 1
700151421916807169 1
Name: tweet_id, Length: 2356, dtype: int64
In [21]:
df1['name']
Out[21]:
0 Phineas
1 Tilly
2 Archie
3 Darla
4 Franklin
5 None
6 Jax
7 None
8 Zoey
9 Cassie
10 Koda
11 Bruno
12 None
13 Ted
14 Stuart
15 Oliver
16 Jim
17 Zeke
18 Ralphus
19 Canela
20 Gerald
21 Jeffrey
22 such
23 Canela
24 None
25 None
26 Maya
27 Mingus
28 Derek
29 Roscoe
...
2326 quite
2327 a
2328 None
2329 None
2330 None
2331 None
2332 None
2333 an
2334 a
2335 an
2336 None
2337 None
2338 None
2339 None
2340 None
2341 None
2342 None
2343 None
2344 None
2345 the
2346 the
2347 a
2348 a
2349 an
2350 a
2351 None
2352 a
2353 a
2354 a
2355 None
Name: name, Length: 2356, dtype: object
In [22]:
df1['name'].value_counts()
Out[22]:
None 745
a 55
Charlie 12
Lucy 11
Cooper 11
Oliver 11
Penny 10
Tucker 10
Lola 10
Winston 9
Bo 9
the 8
Sadie 8
an 7
Toby 7
Buddy 7
Daisy 7
Bailey 7
Rusty 6
Jack 6
Bella 6
Koda 6
Leo 6
Oscar 6
Jax 6
Stanley 6
Scout 6
Milo 6
Dave 6
George 5
...
Ralphy 1
Oreo 1
Eazy 1
Rizzo 1
Keet 1
Carll 1
Crawford 1
Kona 1
Blue 1
Ulysses 1
Reptar 1
Halo 1
Barney 1
Dunkin 1
Filup 1
Ralpher 1
Puff 1
Karl 1
Gert 1
Edd 1
Milky 1
Enchilada 1
Harvey 1
Tyrus 1
Harlso 1
Remy 1
Cilantro 1
Darby 1
Mairi 1
Batdog 1
Name: name, Length: 957, dtype: int64
In [23]:
## Checking if there are any names in lower case
df1.name[df1.name.str.islower()]
Out[23]:
22 such
56 a
118 quite
169 quite
193 quite
335 not
369 one
542 incredibly
649 a
682 mad
759 an
773 very
801 a
819 very
822 just
852 my
924 one
988 not
992 his
993 one
1002 a
1004 a
1017 a
1025 an
1031 very
1040 actually
1049 a
1063 just
1071 getting
1095 mad
...
2191 a
2198 a
2204 an
2211 a
2212 the
2218 a
2222 a
2235 a
2249 a
2255 a
2264 a
2273 a
2287 a
2304 a
2311 a
2314 a
2326 quite
2327 a
2333 an
2334 a
2335 an
2345 the
2346 the
2347 a
2348 a
2349 an
2350 a
2352 a
2353 a
2354 a
Name: name, Length: 109, dtype: object
his, a,the,a,an,quite,such and none should not be in name
In [24]:
df1['source'].value_counts()
Out[24]:
<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> 2221 <a href="http://vine.co" rel="nofollow">Vine - Make a Scene</a> 91 <a href="http://twitter.com" rel="nofollow">Twitter Web Client</a> 33 <a href="https://about.twitter.com/products/tweetdeck" rel="nofollow">TweetDeck</a> 11 Name: source, dtype: int64
In [25]:
## The information about the source is in the url// and is not easily readible
In [26]:
## There are lot of none values in the dataframe
In [27]:
df1['expanded_urls'].head(20)
Out[27]:
0 https://twitter.com/dog_rates/status/892420643... 1 https://twitter.com/dog_rates/status/892177421... 2 https://twitter.com/dog_rates/status/891815181... 3 https://twitter.com/dog_rates/status/891689557... 4 https://twitter.com/dog_rates/status/891327558... 5 https://twitter.com/dog_rates/status/891087950... 6 https://gofundme.com/ydvmve-surgery-for-jax,ht... 7 https://twitter.com/dog_rates/status/890729181... 8 https://twitter.com/dog_rates/status/890609185... 9 https://twitter.com/dog_rates/status/890240255... 10 https://twitter.com/dog_rates/status/890006608... 11 https://twitter.com/dog_rates/status/889880896... 12 https://twitter.com/dog_rates/status/889665388... 13 https://twitter.com/dog_rates/status/889638837... 14 https://twitter.com/dog_rates/status/889531135... 15 https://twitter.com/dog_rates/status/889278841... 16 https://twitter.com/dog_rates/status/888917238... 17 https://twitter.com/dog_rates/status/888804989... 18 https://twitter.com/dog_rates/status/888554962... 19 https://twitter.com/dog_rates/status/887473957... Name: expanded_urls, dtype: object
In [28]:
df1.expanded_urls[df1.expanded_urls.isna()].shape
Out[28]:
(59,)
In [29]:
## Missing values in exapnded url(59)
In [30]:
df1['rating_numerator'].value_counts()
Out[30]:
12 558 11 464 10 461 13 351 9 158 8 102 7 55 14 54 5 37 6 32 3 19 4 17 1 9 2 9 420 2 0 2 15 2 75 2 80 1 20 1 24 1 26 1 44 1 50 1 60 1 165 1 84 1 88 1 144 1 182 1 143 1 666 1 960 1 1776 1 17 1 27 1 45 1 99 1 121 1 204 1 Name: rating_numerator, dtype: int64
In [31]:
df1['rating_denominator'].value_counts()
Out[31]:
10 2333 11 3 50 3 80 2 20 2 2 1 16 1 40 1 70 1 15 1 90 1 110 1 120 1 130 1 150 1 170 1 7 1 0 1 Name: rating_denominator, dtype: int64
In [32]:
df1.query('rating_numerator == 1776')
Out[32]:
| tweet_id | in_reply_to_status_id | in_reply_to_user_id | timestamp | source | text | retweeted_status_id | retweeted_status_user_id | retweeted_status_timestamp | expanded_urls | rating_numerator | rating_denominator | name | doggo | floofer | pupper | puppo | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 979 | 749981277374128128 | NaN | NaN | 2016-07-04 15:00:45 +0000 | <a href="https://about.twitter.com/products/tw... | This is Atticus. He's quite simply America af.... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/749981277... | 1776 | 10 | Atticus | None | None | None | None |
In [33]:
## Incorrect data for the denominator --> the denominator have values other than 10
In [34]:
## Visual Assessment
imagedf1.head(20)
Out[34]:
| tweet_id | jpg_url | img_num | p1 | p1_conf | p1_dog | p2 | p2_conf | p2_dog | p3 | p3_conf | p3_dog | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 666020888022790149 | https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg | 1 | Welsh_springer_spaniel | 0.465074 | True | collie | 0.156665 | True | Shetland_sheepdog | 0.061428 | True |
| 1 | 666029285002620928 | https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg | 1 | redbone | 0.506826 | True | miniature_pinscher | 0.074192 | True | Rhodesian_ridgeback | 0.072010 | True |
| 2 | 666033412701032449 | https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg | 1 | German_shepherd | 0.596461 | True | malinois | 0.138584 | True | bloodhound | 0.116197 | True |
| 3 | 666044226329800704 | https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg | 1 | Rhodesian_ridgeback | 0.408143 | True | redbone | 0.360687 | True | miniature_pinscher | 0.222752 | True |
| 4 | 666049248165822465 | https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg | 1 | miniature_pinscher | 0.560311 | True | Rottweiler | 0.243682 | True | Doberman | 0.154629 | True |
| 5 | 666050758794694657 | https://pbs.twimg.com/media/CT5Jof1WUAEuVxN.jpg | 1 | Bernese_mountain_dog | 0.651137 | True | English_springer | 0.263788 | True | Greater_Swiss_Mountain_dog | 0.016199 | True |
| 6 | 666051853826850816 | https://pbs.twimg.com/media/CT5KoJ1WoAAJash.jpg | 1 | box_turtle | 0.933012 | False | mud_turtle | 0.045885 | False | terrapin | 0.017885 | False |
| 7 | 666055525042405380 | https://pbs.twimg.com/media/CT5N9tpXIAAifs1.jpg | 1 | chow | 0.692517 | True | Tibetan_mastiff | 0.058279 | True | fur_coat | 0.054449 | False |
| 8 | 666057090499244032 | https://pbs.twimg.com/media/CT5PY90WoAAQGLo.jpg | 1 | shopping_cart | 0.962465 | False | shopping_basket | 0.014594 | False | golden_retriever | 0.007959 | True |
| 9 | 666058600524156928 | https://pbs.twimg.com/media/CT5Qw94XAAA_2dP.jpg | 1 | miniature_poodle | 0.201493 | True | komondor | 0.192305 | True | soft-coated_wheaten_terrier | 0.082086 | True |
| 10 | 666063827256086533 | https://pbs.twimg.com/media/CT5Vg_wXIAAXfnj.jpg | 1 | golden_retriever | 0.775930 | True | Tibetan_mastiff | 0.093718 | True | Labrador_retriever | 0.072427 | True |
| 11 | 666071193221509120 | https://pbs.twimg.com/media/CT5cN_3WEAAlOoZ.jpg | 1 | Gordon_setter | 0.503672 | True | Yorkshire_terrier | 0.174201 | True | Pekinese | 0.109454 | True |
| 12 | 666073100786774016 | https://pbs.twimg.com/media/CT5d9DZXAAALcwe.jpg | 1 | Walker_hound | 0.260857 | True | English_foxhound | 0.175382 | True | Ibizan_hound | 0.097471 | True |
| 13 | 666082916733198337 | https://pbs.twimg.com/media/CT5m4VGWEAAtKc8.jpg | 1 | pug | 0.489814 | True | bull_mastiff | 0.404722 | True | French_bulldog | 0.048960 | True |
| 14 | 666094000022159362 | https://pbs.twimg.com/media/CT5w9gUW4AAsBNN.jpg | 1 | bloodhound | 0.195217 | True | German_shepherd | 0.078260 | True | malinois | 0.075628 | True |
| 15 | 666099513787052032 | https://pbs.twimg.com/media/CT51-JJUEAA6hV8.jpg | 1 | Lhasa | 0.582330 | True | Shih-Tzu | 0.166192 | True | Dandie_Dinmont | 0.089688 | True |
| 16 | 666102155909144576 | https://pbs.twimg.com/media/CT54YGiWUAEZnoK.jpg | 1 | English_setter | 0.298617 | True | Newfoundland | 0.149842 | True | borzoi | 0.133649 | True |
| 17 | 666104133288665088 | https://pbs.twimg.com/media/CT56LSZWoAAlJj2.jpg | 1 | hen | 0.965932 | False | cock | 0.033919 | False | partridge | 0.000052 | False |
| 18 | 666268910803644416 | https://pbs.twimg.com/media/CT8QCd1WEAADXws.jpg | 1 | desktop_computer | 0.086502 | False | desk | 0.085547 | False | bookcase | 0.079480 | False |
| 19 | 666273097616637952 | https://pbs.twimg.com/media/CT8T1mtUwAA3aqm.jpg | 1 | Italian_greyhound | 0.176053 | True | toy_terrier | 0.111884 | True | basenji | 0.111152 | True |
In [35]:
imagedf1.tail(20)
Out[35]:
| tweet_id | jpg_url | img_num | p1 | p1_conf | p1_dog | p2 | p2_conf | p2_dog | p3 | p3_conf | p3_dog | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2055 | 888202515573088257 | https://pbs.twimg.com/media/DFDw2tyUQAAAFke.jpg | 2 | Pembroke | 0.809197 | True | Rhodesian_ridgeback | 0.054950 | True | beagle | 0.038915 | True |
| 2056 | 888554962724278272 | https://pbs.twimg.com/media/DFTH_O-UQAACu20.jpg | 3 | Siberian_husky | 0.700377 | True | Eskimo_dog | 0.166511 | True | malamute | 0.111411 | True |
| 2057 | 888804989199671297 | https://pbs.twimg.com/media/DFWra-3VYAA2piG.jpg | 1 | golden_retriever | 0.469760 | True | Labrador_retriever | 0.184172 | True | English_setter | 0.073482 | True |
| 2058 | 888917238123831296 | https://pbs.twimg.com/media/DFYRgsOUQAARGhO.jpg | 1 | golden_retriever | 0.714719 | True | Tibetan_mastiff | 0.120184 | True | Labrador_retriever | 0.105506 | True |
| 2059 | 889278841981685760 | https://pbs.twimg.com/ext_tw_video_thumb/88927... | 1 | whippet | 0.626152 | True | borzoi | 0.194742 | True | Saluki | 0.027351 | True |
| 2060 | 889531135344209921 | https://pbs.twimg.com/media/DFg_2PVW0AEHN3p.jpg | 1 | golden_retriever | 0.953442 | True | Labrador_retriever | 0.013834 | True | redbone | 0.007958 | True |
| 2061 | 889638837579907072 | https://pbs.twimg.com/media/DFihzFfXsAYGDPR.jpg | 1 | French_bulldog | 0.991650 | True | boxer | 0.002129 | True | Staffordshire_bullterrier | 0.001498 | True |
| 2062 | 889665388333682689 | https://pbs.twimg.com/media/DFi579UWsAAatzw.jpg | 1 | Pembroke | 0.966327 | True | Cardigan | 0.027356 | True | basenji | 0.004633 | True |
| 2063 | 889880896479866881 | https://pbs.twimg.com/media/DFl99B1WsAITKsg.jpg | 1 | French_bulldog | 0.377417 | True | Labrador_retriever | 0.151317 | True | muzzle | 0.082981 | False |
| 2064 | 890006608113172480 | https://pbs.twimg.com/media/DFnwSY4WAAAMliS.jpg | 1 | Samoyed | 0.957979 | True | Pomeranian | 0.013884 | True | chow | 0.008167 | True |
| 2065 | 890240255349198849 | https://pbs.twimg.com/media/DFrEyVuW0AAO3t9.jpg | 1 | Pembroke | 0.511319 | True | Cardigan | 0.451038 | True | Chihuahua | 0.029248 | True |
| 2066 | 890609185150312448 | https://pbs.twimg.com/media/DFwUU__XcAEpyXI.jpg | 1 | Irish_terrier | 0.487574 | True | Irish_setter | 0.193054 | True | Chesapeake_Bay_retriever | 0.118184 | True |
| 2067 | 890729181411237888 | https://pbs.twimg.com/media/DFyBahAVwAAhUTd.jpg | 2 | Pomeranian | 0.566142 | True | Eskimo_dog | 0.178406 | True | Pembroke | 0.076507 | True |
| 2068 | 890971913173991426 | https://pbs.twimg.com/media/DF1eOmZXUAALUcq.jpg | 1 | Appenzeller | 0.341703 | True | Border_collie | 0.199287 | True | ice_lolly | 0.193548 | False |
| 2069 | 891087950875897856 | https://pbs.twimg.com/media/DF3HwyEWsAABqE6.jpg | 1 | Chesapeake_Bay_retriever | 0.425595 | True | Irish_terrier | 0.116317 | True | Indian_elephant | 0.076902 | False |
| 2070 | 891327558926688256 | https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg | 2 | basset | 0.555712 | True | English_springer | 0.225770 | True | German_short-haired_pointer | 0.175219 | True |
| 2071 | 891689557279858688 | https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg | 1 | paper_towel | 0.170278 | False | Labrador_retriever | 0.168086 | True | spatula | 0.040836 | False |
| 2072 | 891815181378084864 | https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg | 1 | Chihuahua | 0.716012 | True | malamute | 0.078253 | True | kelpie | 0.031379 | True |
| 2073 | 892177421306343426 | https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg | 1 | Chihuahua | 0.323581 | True | Pekinese | 0.090647 | True | papillon | 0.068957 | True |
| 2074 | 892420643555336193 | https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg | 1 | orange | 0.097049 | False | bagel | 0.085851 | False | banana | 0.076110 | False |
In [36]:
imagedf1.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 2075 entries, 0 to 2074 Data columns (total 12 columns): tweet_id 2075 non-null int64 jpg_url 2075 non-null object img_num 2075 non-null int64 p1 2075 non-null object p1_conf 2075 non-null float64 p1_dog 2075 non-null bool p2 2075 non-null object p2_conf 2075 non-null float64 p2_dog 2075 non-null bool p3 2075 non-null object p3_conf 2075 non-null float64 p3_dog 2075 non-null bool dtypes: bool(3), float64(3), int64(2), object(4) memory usage: 152.1+ KB
In [37]:
## there are no null values but the total count of twitter data is 2356(suspect that 281 records might be retweeted)
In [38]:
## The column not informative (p1,p1_conf,p1_dog,p2,p2_conf,p2_dog,p3,p3_conf,p3_dog)
In [39]:
imagedf1['p1'].value_counts()
Out[39]:
golden_retriever 150
Labrador_retriever 100
Pembroke 89
Chihuahua 83
pug 57
chow 44
Samoyed 43
toy_poodle 39
Pomeranian 38
cocker_spaniel 30
malamute 30
French_bulldog 26
Chesapeake_Bay_retriever 23
miniature_pinscher 23
seat_belt 22
German_shepherd 20
Siberian_husky 20
Staffordshire_bullterrier 20
Cardigan 19
web_site 19
Shetland_sheepdog 18
Maltese_dog 18
Eskimo_dog 18
teddy 18
beagle 18
Rottweiler 17
Lakeland_terrier 17
Shih-Tzu 17
kuvasz 16
Italian_greyhound 16
...
traffic_light 1
envelope 1
soccer_ball 1
African_crocodile 1
cheetah 1
rapeseed 1
guenon 1
flamingo 1
four-poster 1
bighorn 1
mailbox 1
lorikeet 1
remote_control 1
radio_telescope 1
rotisserie 1
boathouse 1
black-footed_ferret 1
washer 1
pool_table 1
snowmobile 1
padlock 1
terrapin 1
lion 1
china_cabinet 1
scorpion 1
Egyptian_cat 1
maillot 1
basketball 1
sea_urchin 1
zebra 1
Name: p1, Length: 378, dtype: int64
In [40]:
## the dog names in the p1_column are in upper and lower case
In [41]:
imagedf1['p2'].value_counts()
Out[41]:
Labrador_retriever 104
golden_retriever 92
Cardigan 73
Chihuahua 44
Pomeranian 42
Chesapeake_Bay_retriever 41
French_bulldog 41
toy_poodle 37
cocker_spaniel 34
Siberian_husky 33
miniature_poodle 33
beagle 28
collie 27
Pembroke 27
Eskimo_dog 27
kuvasz 26
Italian_greyhound 22
Pekinese 21
American_Staffordshire_terrier 21
miniature_pinscher 20
malinois 20
toy_terrier 20
Samoyed 20
chow 20
Norwegian_elkhound 19
Boston_bull 19
Staffordshire_bullterrier 18
Irish_terrier 17
pug 17
Shih-Tzu 16
...
stingray 1
saltshaker 1
standard_schnauzer 1
hotdog 1
space_heater 1
racket 1
grey_fox 1
spatula 1
sea_urchin 1
hay 1
dugong 1
ice_lolly 1
pier 1
necklace 1
wood_rabbit 1
breastplate 1
boathouse 1
neck_brace 1
washbasin 1
streetcar 1
volcano 1
leafhopper 1
oxygen_mask 1
mailbox 1
timber_wolf 1
tick 1
rifle 1
comic_book 1
hand-held_computer 1
African_hunting_dog 1
Name: p2, Length: 405, dtype: int64
In [42]:
## the dog names in column p2 are in upper and lower case
In [43]:
imagedf1['p3'].value_counts()
Out[43]:
Labrador_retriever 79
Chihuahua 58
golden_retriever 48
Eskimo_dog 38
kelpie 35
kuvasz 34
chow 32
Staffordshire_bullterrier 32
cocker_spaniel 31
beagle 31
Pomeranian 29
Pekinese 29
toy_poodle 29
Great_Pyrenees 27
Pembroke 27
Chesapeake_Bay_retriever 27
malamute 26
French_bulldog 26
American_Staffordshire_terrier 24
Cardigan 23
pug 23
basenji 21
bull_mastiff 20
toy_terrier 20
Siberian_husky 19
Boston_bull 17
Shetland_sheepdog 17
doormat 16
Lakeland_terrier 16
boxer 16
..
switch 1
nipple 1
space_shuttle 1
Sussex_spaniel 1
pretzel 1
go-kart 1
parachute 1
pier 1
viaduct 1
valley 1
dugong 1
mitten 1
padlock 1
canoe 1
hand-held_computer 1
wok 1
meerkat 1
drumstick 1
rifle 1
theater_curtain 1
moped 1
spatula 1
vacuum 1
neck_brace 1
shower_cap 1
bullfrog 1
electric_fan 1
European_fire_salamander 1
soap_dispenser 1
zebra 1
Name: p3, Length: 408, dtype: int64
In [44]:
## the dog names in column p3 are in upper and lower case
In [45]:
## Checking for duplicate tweet_id column
imagedf1.tweet_id.value_counts()
Out[45]:
685532292383666176 1
826598365270007810 1
692158366030913536 1
714606013974974464 1
715696743237730304 1
776477788987613185 1
772114945936949249 1
699775878809702401 1
780858289093574656 1
700462010979500032 1
732726085725589504 1
738883359779196928 1
798644042770751489 1
743510151680958465 1
837012587749474308 1
833722901757046785 1
668620235289837568 1
842765311967449089 1
685315239903100929 1
673686845050527744 1
680473011644985345 1
666051853826850816 1
675853064436391936 1
693231807727280129 1
705475953783398401 1
829449946868879360 1
759923798737051648 1
667160273090932737 1
680934982542561280 1
743545585370791937 1
..
794926597468000259 1
776113305656188928 1
825026590719483904 1
834209720923721728 1
775733305207554048 1
669564461267722241 1
879492040517615616 1
720775346191278080 1
666362758909284353 1
750506206503038976 1
693155686491000832 1
793601777308463104 1
740373189193256964 1
754482103782404096 1
881536004380872706 1
843604394117681152 1
748307329658011649 1
759846353224826880 1
885984800019947520 1
773922284943896577 1
666345417576210432 1
837482249356513284 1
812781120811126785 1
870804317367881728 1
790698755171364864 1
816829038950027264 1
847971574464610304 1
713175907180089344 1
670338931251150849 1
700151421916807169 1
Name: tweet_id, Length: 2075, dtype: int64
In [46]:
## there are no duplicated tweet_id's
In [47]:
##Checking for duplicated url
imagedf1[imagedf1['jpg_url'].duplicated()]
Out[47]:
| tweet_id | jpg_url | img_num | p1 | p1_conf | p1_dog | p2 | p2_conf | p2_dog | p3 | p3_conf | p3_dog | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1297 | 752309394570878976 | https://pbs.twimg.com/ext_tw_video_thumb/67535... | 1 | upright | 0.303415 | False | golden_retriever | 0.181351 | True | Brittany_spaniel | 0.162084 | True |
| 1315 | 754874841593970688 | https://pbs.twimg.com/media/CWza7kpWcAAdYLc.jpg | 1 | pug | 0.272205 | True | bull_mastiff | 0.251530 | True | bath_towel | 0.116806 | False |
| 1333 | 757729163776290825 | https://pbs.twimg.com/media/CWyD2HGUYAQ1Xa7.jpg | 2 | cash_machine | 0.802333 | False | schipperke | 0.045519 | True | German_shepherd | 0.023353 | True |
| 1345 | 759159934323924993 | https://pbs.twimg.com/media/CU1zsMSUAAAS0qW.jpg | 1 | Irish_terrier | 0.254856 | True | briard | 0.227716 | True | soft-coated_wheaten_terrier | 0.223263 | True |
| 1349 | 759566828574212096 | https://pbs.twimg.com/media/CkNjahBXAAQ2kWo.jpg | 1 | Labrador_retriever | 0.967397 | True | golden_retriever | 0.016641 | True | ice_bear | 0.014858 | False |
| 1364 | 761371037149827077 | https://pbs.twimg.com/tweet_video_thumb/CeBym7... | 1 | brown_bear | 0.713293 | False | Indian_elephant | 0.172844 | False | water_buffalo | 0.038902 | False |
| 1368 | 761750502866649088 | https://pbs.twimg.com/media/CYLDikFWEAAIy1y.jpg | 1 | golden_retriever | 0.586937 | True | Labrador_retriever | 0.398260 | True | kuvasz | 0.005410 | True |
| 1387 | 766078092750233600 | https://pbs.twimg.com/media/ChK1tdBWwAQ1flD.jpg | 1 | toy_poodle | 0.420463 | True | miniature_poodle | 0.132640 | True | Chesapeake_Bay_retriever | 0.121523 | True |
| 1407 | 770093767776997377 | https://pbs.twimg.com/media/CkjMx99UoAM2B1a.jpg | 1 | golden_retriever | 0.843799 | True | Labrador_retriever | 0.052956 | True | kelpie | 0.035711 | True |
| 1417 | 771171053431250945 | https://pbs.twimg.com/media/CVgdFjNWEAAxmbq.jpg | 3 | Samoyed | 0.978833 | True | Pomeranian | 0.012763 | True | Eskimo_dog | 0.001853 | True |
| 1427 | 772615324260794368 | https://pbs.twimg.com/media/Cp6db4-XYAAMmqL.jpg | 1 | dalmatian | 0.556595 | True | whippet | 0.151047 | True | American_Staffordshire_terrier | 0.096435 | True |
| 1446 | 775898661951791106 | https://pbs.twimg.com/media/CiyHLocU4AI2pJu.jpg | 1 | golden_retriever | 0.945523 | True | Labrador_retriever | 0.042319 | True | doormat | 0.003956 | False |
| 1453 | 776819012571455488 | https://pbs.twimg.com/media/CW88XN4WsAAlo8r.jpg | 3 | Chihuahua | 0.346545 | True | dalmatian | 0.166246 | True | toy_terrier | 0.117502 | True |
| 1456 | 777641927919427584 | https://pbs.twimg.com/media/CmoPdmHW8AAi8BI.jpg | 1 | golden_retriever | 0.964929 | True | Labrador_retriever | 0.011584 | True | refrigerator | 0.007499 | False |
| 1463 | 778396591732486144 | https://pbs.twimg.com/media/CcG07BYW0AErrC9.jpg | 1 | hippopotamus | 0.581403 | False | doormat | 0.152445 | False | sea_lion | 0.026364 | False |
| 1476 | 780496263422808064 | https://pbs.twimg.com/media/Ck2d7tJWUAEPTL3.jpg | 1 | pug | 0.997310 | True | Brabancon_griffon | 0.001186 | True | French_bulldog | 0.000428 | True |
| 1487 | 782021823840026624 | https://pbs.twimg.com/media/CdHwZd0VIAA4792.jpg | 1 | golden_retriever | 0.383223 | True | cocker_spaniel | 0.165930 | True | Chesapeake_Bay_retriever | 0.118199 | True |
| 1495 | 783347506784731136 | https://pbs.twimg.com/media/CVuQ2LeUsAAIe3s.jpg | 1 | Cardigan | 0.611525 | True | Pembroke | 0.368566 | True | Chihuahua | 0.003330 | True |
| 1510 | 786036967502913536 | https://pbs.twimg.com/media/CtKHLuCWYAA2TTs.jpg | 1 | golden_retriever | 0.993830 | True | cocker_spaniel | 0.003143 | True | Great_Pyrenees | 0.000917 | True |
| 1522 | 788070120937619456 | https://pbs.twimg.com/media/Co-hmcYXYAASkiG.jpg | 1 | golden_retriever | 0.735163 | True | Sussex_spaniel | 0.064897 | True | Labrador_retriever | 0.047704 | True |
| 1538 | 790723298204217344 | https://pbs.twimg.com/media/CvaYgDOWgAEfjls.jpg | 1 | tub | 0.479477 | False | bathtub | 0.325106 | False | golden_retriever | 0.078530 | True |
| 1541 | 791026214425268224 | https://pbs.twimg.com/media/CpmyNumW8AAAJGj.jpg | 1 | malamute | 0.375098 | True | jean | 0.069362 | False | keeshond | 0.050528 | True |
| 1564 | 793614319594401792 | https://pbs.twimg.com/media/CvyVxQRWEAAdSZS.jpg | 1 | golden_retriever | 0.705092 | True | Labrador_retriever | 0.219721 | True | kuvasz | 0.015965 | True |
| 1569 | 794355576146903043 | https://pbs.twimg.com/media/CvJCabcWgAIoUxW.jpg | 1 | cocker_spaniel | 0.500509 | True | golden_retriever | 0.272734 | True | jigsaw_puzzle | 0.041476 | False |
| 1571 | 794983741416415232 | https://pbs.twimg.com/media/CvT6IV6WEAQhhV5.jpg | 3 | schipperke | 0.363272 | True | kelpie | 0.197021 | True | Norwegian_elkhound | 0.151024 | True |
| 1579 | 796177847564038144 | https://pbs.twimg.com/media/Cwx99rpW8AMk_Ie.jpg | 1 | golden_retriever | 0.600276 | True | Labrador_retriever | 0.140798 | True | seat_belt | 0.087355 | False |
| 1588 | 798340744599797760 | https://pbs.twimg.com/media/CrXhIqBW8AA6Bse.jpg | 1 | papillon | 0.533180 | True | collie | 0.192031 | True | Border_collie | 0.121626 | True |
| 1589 | 798628517273620480 | https://pbs.twimg.com/media/CUN4Or5UAAAa5K4.jpg | 1 | beagle | 0.636169 | True | Labrador_retriever | 0.119256 | True | golden_retriever | 0.082549 | True |
| 1590 | 798644042770751489 | https://pbs.twimg.com/media/CU3mITUWIAAfyQS.jpg | 1 | English_springer | 0.403698 | True | Brittany_spaniel | 0.347609 | True | Welsh_springer_spaniel | 0.137186 | True |
| 1591 | 798665375516884993 | https://pbs.twimg.com/media/CVMOlMiWwAA4Yxl.jpg | 1 | chow | 0.243529 | True | hamster | 0.227150 | False | Pomeranian | 0.056057 | True |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1619 | 802624713319034886 | https://pbs.twimg.com/media/CsrjryzWgAAZY00.jpg | 1 | cocker_spaniel | 0.253442 | True | golden_retriever | 0.162850 | True | otterhound | 0.110921 | True |
| 1624 | 803692223237865472 | https://pbs.twimg.com/media/CZhn-QAWwAASQan.jpg | 1 | Lakeland_terrier | 0.530104 | True | Irish_terrier | 0.197314 | True | Airedale | 0.082515 | True |
| 1627 | 804413760345620481 | https://pbs.twimg.com/media/CuRDF-XWcAIZSer.jpg | 1 | chow | 0.090341 | True | binoculars | 0.083499 | False | Irish_setter | 0.077456 | True |
| 1634 | 805958939288408065 | https://pbs.twimg.com/media/CtzKC7zXEAALfSo.jpg | 1 | Irish_setter | 0.574557 | True | golden_retriever | 0.339251 | True | seat_belt | 0.046108 | False |
| 1636 | 806242860592926720 | https://pbs.twimg.com/media/Ct72q9jWcAAhlnw.jpg | 2 | Cardigan | 0.593858 | True | Shetland_sheepdog | 0.130611 | True | Pembroke | 0.100842 | True |
| 1640 | 807059379405148160 | https://pbs.twimg.com/media/Ct2qO5PXEAE6eB0.jpg | 1 | seat_belt | 0.474292 | False | golden_retriever | 0.171393 | True | Labrador_retriever | 0.110592 | True |
| 1645 | 808134635716833280 | https://pbs.twimg.com/media/Cx5R8wPVEAALa9r.jpg | 1 | cocker_spaniel | 0.740220 | True | Dandie_Dinmont | 0.061604 | True | English_setter | 0.041331 | True |
| 1652 | 809808892968534016 | https://pbs.twimg.com/media/CwS4aqZXUAAe3IO.jpg | 1 | Labrador_retriever | 0.861651 | True | golden_retriever | 0.044462 | True | Staffordshire_bullterrier | 0.016497 | True |
| 1683 | 813944609378369540 | https://pbs.twimg.com/media/Cveg1-NXgAASaaT.jpg | 1 | Labrador_retriever | 0.427742 | True | Great_Dane | 0.190503 | True | curly-coated_retriever | 0.146427 | True |
| 1693 | 816014286006976512 | https://pbs.twimg.com/media/CiibOMzUYAA9Mxz.jpg | 1 | English_setter | 0.677408 | True | Border_collie | 0.052724 | True | cocker_spaniel | 0.048572 | True |
| 1699 | 816829038950027264 | https://pbs.twimg.com/media/CvoBPWRWgAA4het.jpg | 1 | dishwasher | 0.700466 | False | golden_retriever | 0.245773 | True | chow | 0.039012 | True |
| 1703 | 817181837579653120 | https://pbs.twimg.com/ext_tw_video_thumb/81596... | 1 | Tibetan_mastiff | 0.506312 | True | Tibetan_terrier | 0.295690 | True | otterhound | 0.036251 | True |
| 1712 | 818588835076603904 | https://pbs.twimg.com/media/Crwxb5yWgAAX5P_.jpg | 1 | Norwegian_elkhound | 0.372202 | True | Chesapeake_Bay_retriever | 0.137187 | True | malamute | 0.071436 | True |
| 1717 | 819015331746349057 | https://pbs.twimg.com/media/C12x-JTVIAAzdfl.jpg | 4 | prison | 0.907083 | False | palace | 0.020089 | False | umbrella | 0.007850 | False |
| 1718 | 819015337530290176 | https://pbs.twimg.com/media/C12whDoVEAALRxa.jpg | 1 | standard_poodle | 0.351308 | True | toy_poodle | 0.271929 | True | Tibetan_terrier | 0.094759 | True |
| 1727 | 820446719150292993 | https://pbs.twimg.com/media/CxqsX-8XUAAEvjD.jpg | 3 | golden_retriever | 0.938048 | True | kuvasz | 0.025119 | True | Labrador_retriever | 0.022977 | True |
| 1736 | 821813639212650496 | https://pbs.twimg.com/media/CtVAvX-WIAAcGTf.jpg | 1 | Saint_Bernard | 0.995143 | True | Cardigan | 0.003044 | True | English_springer | 0.001050 | True |
| 1742 | 822647212903690241 | https://pbs.twimg.com/media/C2oRbOuWEAAbVSl.jpg | 1 | Samoyed | 0.416769 | True | malamute | 0.252706 | True | kuvasz | 0.157028 | True |
| 1746 | 823269594223824897 | https://pbs.twimg.com/media/C2kzTGxWEAEOpPL.jpg | 1 | Samoyed | 0.585441 | True | Pomeranian | 0.193654 | True | Arctic_fox | 0.071648 | False |
| 1755 | 824796380199809024 | https://pbs.twimg.com/media/CwiuEJmW8AAZnit.jpg | 2 | gas_pump | 0.676439 | False | harvester | 0.049995 | False | swing | 0.044660 | False |
| 1789 | 829878982036299777 | https://pbs.twimg.com/media/C3nygbBWQAAjwcW.jpg | 1 | golden_retriever | 0.617389 | True | Labrador_retriever | 0.337053 | True | tennis_ball | 0.008554 | False |
| 1803 | 832040443403784192 | https://pbs.twimg.com/media/Cq9guJ5WgAADfpF.jpg | 1 | miniature_pinscher | 0.796313 | True | Chihuahua | 0.155413 | True | Staffordshire_bullterrier | 0.030943 | True |
| 1804 | 832215726631055365 | https://pbs.twimg.com/media/CwJR1okWIAA6XMp.jpg | 1 | Afghan_hound | 0.274637 | True | borzoi | 0.142204 | True | doormat | 0.109677 | False |
| 1858 | 841833993020538882 | https://pbs.twimg.com/ext_tw_video_thumb/81742... | 1 | ice_bear | 0.336200 | False | Samoyed | 0.201358 | True | Eskimo_dog | 0.186789 | True |
| 1864 | 842892208864923648 | https://pbs.twimg.com/ext_tw_video_thumb/80710... | 1 | Chihuahua | 0.505370 | True | Pomeranian | 0.120358 | True | toy_terrier | 0.077008 | True |
| 1903 | 851953902622658560 | https://pbs.twimg.com/media/C4KHj-nWQAA3poV.jpg | 1 | Staffordshire_bullterrier | 0.757547 | True | American_Staffordshire_terrier | 0.149950 | True | Chesapeake_Bay_retriever | 0.047523 | True |
| 1944 | 861769973181624320 | https://pbs.twimg.com/media/CzG425nWgAAnP7P.jpg | 2 | Arabian_camel | 0.366248 | False | house_finch | 0.209852 | False | cocker_spaniel | 0.046403 | True |
| 1992 | 873697596434513921 | https://pbs.twimg.com/media/DA7iHL5U0AA1OQo.jpg | 1 | laptop | 0.153718 | False | French_bulldog | 0.099984 | True | printer | 0.077130 | False |
| 2041 | 885311592912609280 | https://pbs.twimg.com/media/C4bTH6nWMAAX_bJ.jpg | 1 | Labrador_retriever | 0.908703 | True | seat_belt | 0.057091 | False | pug | 0.011933 | True |
| 2055 | 888202515573088257 | https://pbs.twimg.com/media/DFDw2tyUQAAAFke.jpg | 2 | Pembroke | 0.809197 | True | Rhodesian_ridgeback | 0.054950 | True | beagle | 0.038915 | True |
66 rows × 12 columns
In [48]:
## There are 66 duplicated columns for the jpg url
In [49]:
## Checking for common columns in df1 and imagedf1 columns
two_columns= pd.Series(list(df1)+list(imagedf1))
two_columns[two_columns.duplicated()]
Out[49]:
17 tweet_id dtype: object
In [50]:
## tweet_id is the common column in both the dataset
Tweets_api¶
In [51]:
tweets_api.head(20)
Out[51]:
| id | favorite_count | retweet_count | |
|---|---|---|---|
| 0 | 892420643555336193 | 39467 | 8853 |
| 1 | 892177421306343426 | 33819 | 6514 |
| 2 | 891815181378084864 | 25461 | 4328 |
| 3 | 891689557279858688 | 42908 | 8964 |
| 4 | 891327558926688256 | 41048 | 9774 |
| 5 | 891087950875897856 | 20562 | 3261 |
| 6 | 890971913173991426 | 12041 | 2158 |
| 7 | 890729181411237888 | 56848 | 16716 |
| 8 | 890609185150312448 | 28226 | 4429 |
| 9 | 890240255349198849 | 32467 | 7711 |
| 10 | 890006608113172480 | 31166 | 7624 |
| 11 | 889880896479866881 | 28268 | 5156 |
| 12 | 889665388333682689 | 38818 | 8538 |
| 13 | 889638837579907072 | 27672 | 4735 |
| 14 | 889531135344209921 | 15359 | 2321 |
| 15 | 889278841981685760 | 25652 | 5637 |
| 16 | 888917238123831296 | 29611 | 4709 |
| 17 | 888804989199671297 | 26080 | 4559 |
| 18 | 888554962724278272 | 20290 | 3732 |
| 19 | 888078434458587136 | 22201 | 3653 |
In [52]:
tweets_api.tail(20)
Out[52]:
| id | favorite_count | retweet_count | |
|---|---|---|---|
| 2334 | 666273097616637952 | 184 | 82 |
| 2335 | 666268910803644416 | 108 | 37 |
| 2336 | 666104133288665088 | 14765 | 6871 |
| 2337 | 666102155909144576 | 81 | 16 |
| 2338 | 666099513787052032 | 164 | 73 |
| 2339 | 666094000022159362 | 169 | 79 |
| 2340 | 666082916733198337 | 121 | 47 |
| 2341 | 666073100786774016 | 335 | 174 |
| 2342 | 666071193221509120 | 154 | 67 |
| 2343 | 666063827256086533 | 496 | 232 |
| 2344 | 666058600524156928 | 115 | 61 |
| 2345 | 666057090499244032 | 304 | 146 |
| 2346 | 666055525042405380 | 448 | 261 |
| 2347 | 666051853826850816 | 1253 | 879 |
| 2348 | 666050758794694657 | 136 | 60 |
| 2349 | 666049248165822465 | 111 | 41 |
| 2350 | 666044226329800704 | 311 | 147 |
| 2351 | 666033412701032449 | 128 | 47 |
| 2352 | 666029285002620928 | 132 | 48 |
| 2353 | 666020888022790149 | 2535 | 532 |
In [53]:
tweets_api.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 2354 entries, 0 to 2353 Data columns (total 3 columns): id 2354 non-null int64 favorite_count 2354 non-null int64 retweet_count 2354 non-null int64 dtypes: int64(3) memory usage: 55.2 KB
In [54]:
tweets_api.favorite_count.value_counts().sort_values()
Out[54]:
19163 1
1817 1
3864 1
262 1
1813 1
1096 1
9997 1
3852 1
1803 1
5897 1
3848 1
1799 1
1526 1
565 1
7938 1
857 1
8910 1
2441 1
20208 1
7916 1
1771 1
3818 1
9959 1
7908 1
1763 1
1761 1
28382 1
14081 1
10085 1
1208 1
...
13755 2
1674 2
4578 2
2384 2
1074 2
8209 2
2906 2
327 2
3999 2
2046 2
20275 2
8945 2
2370 2
4228 2
2044 2
2979 2
1253 2
5792 2
592 2
496 2
931 2
610 3
345 3
2918 3
1339 3
2176 3
2768 3
2706 3
1691 3
0 179
Name: favorite_count, Length: 2007, dtype: int64
In [55]:
tweets_api['retweet_count'].value_counts().sort_values()
Out[55]:
274 1
2610 1
561 1
2608 1
4653 1
2602 1
479 1
1055 1
547 1
6690 1
4637 1
563 1
1059 1
537 1
535 1
27680 1
477 1
3130 1
475 1
11330 1
4533 1
433 1
429 1
2586 1
565 1
5159 1
366 1
4598 1
1149 1
285 1
..
231 3
888 3
119 3
896 3
135 3
836 3
792 3
776 3
426 3
4319 3
1225 3
183 4
146 4
61 4
748 4
2243 4
336 4
179 4
819 4
265 4
115 4
71 4
1124 4
542 4
577 4
516 4
1207 4
83 5
3652 5
1972 5
Name: retweet_count, Length: 1724, dtype: int64
In [56]:
sum(tweets_api['id'].duplicated())
Out[56]:
0
In [57]:
## Change the id column to tweet_id (only for remembering)
In [58]:
tweets_api.rename(columns={'id': 'tweet_id'},inplace=True)
Observations¶
Tideness issues¶
- There should only one table for imagedf1,tweet_json,df1(twitter_archive)
- The columns doggo,pupper,floofer, puppo should be one column (type)
Quality issues¶
twitter_archive(df1)
- Null Values (in_reply_to_status_id,in_reply_to_user_id, retweeted_status_user_id,retweeted_status_user_id,retweeted_status_timestamp ) as they are not required for analysis and they have a lot of null values
- Remove the 181 values in retweeted_status_id as we are only interested in original tweets
- Erroneous data timestamp (It is in string format)
- Wrong Denominator rating (There is denominator rating other than 10)
- The exapnded_url has some missing values, the url with missing values; means that there are no images and we should not be using the data with missing url's
- Incorrect dog names (vowels and other conjusction's in dog name)
- There are a lot of none values in dog type's (puppo,doggo,floofer,doggo,pupper)
- url not into human readible form
image_prediction(imagedf1)
- The column names p1,p2,p3 are not very informative
- The dog type have uppercase and lowercase names
Cleaning¶
During the cleaning process the first part is tackle the Tideness issues and then the quality issues
In [59]:
## Making the copy of the dataframes
df1_clean = df1.copy()
imagedf1_clean = imagedf1.copy()
tweets_api_clean = tweets_api.copy()
In [60]:
##Checking
df1_clean.head()
Out[60]:
| tweet_id | in_reply_to_status_id | in_reply_to_user_id | timestamp | source | text | retweeted_status_id | retweeted_status_user_id | retweeted_status_timestamp | expanded_urls | rating_numerator | rating_denominator | name | doggo | floofer | pupper | puppo | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 892420643555336193 | NaN | NaN | 2017-08-01 16:23:56 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Phineas. He's a mystical boy. Only eve... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/892420643... | 13 | 10 | Phineas | None | None | None | None |
| 1 | 892177421306343426 | NaN | NaN | 2017-08-01 00:17:27 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Tilly. She's just checking pup on you.... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/892177421... | 13 | 10 | Tilly | None | None | None | None |
| 2 | 891815181378084864 | NaN | NaN | 2017-07-31 00:18:03 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Archie. He is a rare Norwegian Pouncin... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/891815181... | 12 | 10 | Archie | None | None | None | None |
| 3 | 891689557279858688 | NaN | NaN | 2017-07-30 15:58:51 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Darla. She commenced a snooze mid meal... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/891689557... | 13 | 10 | Darla | None | None | None | None |
| 4 | 891327558926688256 | NaN | NaN | 2017-07-29 16:00:24 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Franklin. He would like you to stop ca... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/891327558... | 12 | 10 | Franklin | None | None | None | None |
In [61]:
imagedf1_clean.head(2)
Out[61]:
| tweet_id | jpg_url | img_num | p1 | p1_conf | p1_dog | p2 | p2_conf | p2_dog | p3 | p3_conf | p3_dog | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 666020888022790149 | https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg | 1 | Welsh_springer_spaniel | 0.465074 | True | collie | 0.156665 | True | Shetland_sheepdog | 0.061428 | True |
| 1 | 666029285002620928 | https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg | 1 | redbone | 0.506826 | True | miniature_pinscher | 0.074192 | True | Rhodesian_ridgeback | 0.072010 | True |
In [62]:
tweets_api_clean.head()
Out[62]:
| tweet_id | favorite_count | retweet_count | |
|---|---|---|---|
| 0 | 892420643555336193 | 39467 | 8853 |
| 1 | 892177421306343426 | 33819 | 6514 |
| 2 | 891815181378084864 | 25461 | 4328 |
| 3 | 891689557279858688 | 42908 | 8964 |
| 4 | 891327558926688256 | 41048 | 9774 |
Define¶
- Make the columns doggo,floofer, pupper, puppo into one column using pandas melt feature
Code¶
In [63]:
df1_clean= pd.melt(df1_clean,id_vars=['tweet_id','in_reply_to_status_id','in_reply_to_user_id','timestamp','source','text','retweeted_status_id','retweeted_status_user_id','retweeted_status_timestamp'
,'expanded_urls','rating_numerator','rating_denominator','name'] ,
value_vars=['doggo','floofer','pupper','puppo'],var_name='stages',value_name='dog_stages')
In [64]:
df1_clean.drop('stages',axis=1,inplace=True)
In [65]:
### Checking for duplicated values
sum(df1_clean.duplicated())
Out[65]:
6674
In [66]:
### Removing duplicated values
df1_clean.drop_duplicates(inplace=True)
Testing¶
In [67]:
df1_clean.shape
Out[67]:
(2750, 14)
In [68]:
df1_clean.head()
Out[68]:
| tweet_id | in_reply_to_status_id | in_reply_to_user_id | timestamp | source | text | retweeted_status_id | retweeted_status_user_id | retweeted_status_timestamp | expanded_urls | rating_numerator | rating_denominator | name | dog_stages | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 892420643555336193 | NaN | NaN | 2017-08-01 16:23:56 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Phineas. He's a mystical boy. Only eve... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/892420643... | 13 | 10 | Phineas | None |
| 1 | 892177421306343426 | NaN | NaN | 2017-08-01 00:17:27 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Tilly. She's just checking pup on you.... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/892177421... | 13 | 10 | Tilly | None |
| 2 | 891815181378084864 | NaN | NaN | 2017-07-31 00:18:03 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Archie. He is a rare Norwegian Pouncin... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/891815181... | 12 | 10 | Archie | None |
| 3 | 891689557279858688 | NaN | NaN | 2017-07-30 15:58:51 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Darla. She commenced a snooze mid meal... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/891689557... | 13 | 10 | Darla | None |
| 4 | 891327558926688256 | NaN | NaN | 2017-07-29 16:00:24 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Franklin. He would like you to stop ca... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/891327558... | 12 | 10 | Franklin | None |
In [69]:
df1_clean.sample(5)
Out[69]:
| tweet_id | in_reply_to_status_id | in_reply_to_user_id | timestamp | source | text | retweeted_status_id | retweeted_status_user_id | retweeted_status_timestamp | expanded_urls | rating_numerator | rating_denominator | name | dog_stages | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2189 | 668967877119254528 | 6.689207e+17 | 21435658.0 | 2015-11-24 01:42:25 +0000 | <a href="http://twitter.com/download/iphone" r... | 12/10 good shit Bubka\n@wane15 | NaN | NaN | NaN | NaN | 12 | 10 | None | None |
| 971 | 750101899009982464 | NaN | NaN | 2016-07-04 23:00:03 +0000 | <a href="http://twitter.com/download/iphone" r... | Meet Lilah. She agreed on one quick pic. Now s... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/750101899... | 11 | 10 | Lilah | None |
| 682 | 788552643979468800 | NaN | NaN | 2016-10-19 01:29:35 +0000 | <a href="http://twitter.com/download/iphone" r... | RT @dog_rates: Say hello to mad pupper. You kn... | 7.363926e+17 | 4.196984e+09 | 2016-05-28 03:04:00 +0000 | https://vine.co/v/iEggaEOiLO3,https://vine.co/... | 13 | 10 | mad | None |
| 1181 | 719332531645071360 | NaN | NaN | 2016-04-11 01:13:34 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Pippa. She managed to start the car bu... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/719332531... | 11 | 10 | Pippa | None |
| 1739 | 679511351870550016 | NaN | NaN | 2015-12-23 03:58:25 +0000 | <a href="http://twitter.com/download/iphone" r... | Say hello to William. He makes fun of others b... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/679511351... | 7 | 10 | William | None |
Define¶
- Merge all the dataframes and make them into one dataframe name it as twitter_master_clean
Code¶
In [70]:
## Creating an abc dataframe by merging df1_clean and imagedf1_clean
abc =pd.merge(df1_clean,imagedf1_clean,on='tweet_id',how='inner')
In [71]:
### Merging the abc dataframe with tweet_json to make on dataframe twitter_master_clean
twitter_master_clean = pd.merge(abc,tweets_api_clean,on='tweet_id',how='inner')
Testing¶
In [72]:
abc.shape
Out[72]:
(2409, 25)
In [73]:
abc.columns
Out[73]:
Index(['tweet_id', 'in_reply_to_status_id', 'in_reply_to_user_id', 'timestamp',
'source', 'text', 'retweeted_status_id', 'retweeted_status_user_id',
'retweeted_status_timestamp', 'expanded_urls', 'rating_numerator',
'rating_denominator', 'name', 'dog_stages', 'jpg_url', 'img_num', 'p1',
'p1_conf', 'p1_dog', 'p2', 'p2_conf', 'p2_dog', 'p3', 'p3_conf',
'p3_dog'],
dtype='object')
In [74]:
twitter_master_clean.head()
Out[74]:
| tweet_id | in_reply_to_status_id | in_reply_to_user_id | timestamp | source | text | retweeted_status_id | retweeted_status_user_id | retweeted_status_timestamp | expanded_urls | ... | p1_conf | p1_dog | p2 | p2_conf | p2_dog | p3 | p3_conf | p3_dog | favorite_count | retweet_count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 892420643555336193 | NaN | NaN | 2017-08-01 16:23:56 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Phineas. He's a mystical boy. Only eve... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/892420643... | ... | 0.097049 | False | bagel | 0.085851 | False | banana | 0.076110 | False | 39467 | 8853 |
| 1 | 892177421306343426 | NaN | NaN | 2017-08-01 00:17:27 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Tilly. She's just checking pup on you.... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/892177421... | ... | 0.323581 | True | Pekinese | 0.090647 | True | papillon | 0.068957 | True | 33819 | 6514 |
| 2 | 891815181378084864 | NaN | NaN | 2017-07-31 00:18:03 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Archie. He is a rare Norwegian Pouncin... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/891815181... | ... | 0.716012 | True | malamute | 0.078253 | True | kelpie | 0.031379 | True | 25461 | 4328 |
| 3 | 891689557279858688 | NaN | NaN | 2017-07-30 15:58:51 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Darla. She commenced a snooze mid meal... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/891689557... | ... | 0.170278 | False | Labrador_retriever | 0.168086 | True | spatula | 0.040836 | False | 42908 | 8964 |
| 4 | 891327558926688256 | NaN | NaN | 2017-07-29 16:00:24 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Franklin. He would like you to stop ca... | NaN | NaN | NaN | https://twitter.com/dog_rates/status/891327558... | ... | 0.555712 | True | English_springer | 0.225770 | True | German_short-haired_pointer | 0.175219 | True | 41048 | 9774 |
5 rows × 27 columns
In [75]:
twitter_master_clean.columns
Out[75]:
Index(['tweet_id', 'in_reply_to_status_id', 'in_reply_to_user_id', 'timestamp',
'source', 'text', 'retweeted_status_id', 'retweeted_status_user_id',
'retweeted_status_timestamp', 'expanded_urls', 'rating_numerator',
'rating_denominator', 'name', 'dog_stages', 'jpg_url', 'img_num', 'p1',
'p1_conf', 'p1_dog', 'p2', 'p2_conf', 'p2_dog', 'p3', 'p3_conf',
'p3_dog', 'favorite_count', 'retweet_count'],
dtype='object')
In [76]:
twitter_master_clean.shape
Out[76]:
(2406, 27)
Quality¶
Define¶
Filter the tweet_master_clean dataframe to remove the 181 values in retweeted_status_id as we are only interested in original tweets
Code¶
In [77]:
## Checting for null values. Instead of 181, 95 values are left because of the merge operations
sum(twitter_master_clean.retweeted_status_id.notnull())
Out[77]:
95
In [78]:
twitter_master_clean.drop(twitter_master_clean[twitter_master_clean.retweeted_status_id.notnull()].index,inplace=True)
Testing¶
In [79]:
twitter_master_clean.shape
Out[79]:
(2311, 27)
In [80]:
sum(twitter_master_clean.retweeted_status_id.notnull())
Out[80]:
0
Define
- Dropping the columns not required for ananlysis ( (in_reply_to_status_id,in_reply_to_user_id, retweeted_status_user_id,retweeted_status_user_id,retweeted_status_timestamp )
In [81]:
#### Code
In [82]:
twitter_master_clean.columns
Out[82]:
Index(['tweet_id', 'in_reply_to_status_id', 'in_reply_to_user_id', 'timestamp',
'source', 'text', 'retweeted_status_id', 'retweeted_status_user_id',
'retweeted_status_timestamp', 'expanded_urls', 'rating_numerator',
'rating_denominator', 'name', 'dog_stages', 'jpg_url', 'img_num', 'p1',
'p1_conf', 'p1_dog', 'p2', 'p2_conf', 'p2_dog', 'p3', 'p3_conf',
'p3_dog', 'favorite_count', 'retweet_count'],
dtype='object')
In [83]:
twitter_master_clean.drop(['in_reply_to_status_id','in_reply_to_user_id', 'retweeted_status_user_id',
'retweeted_status_user_id','retweeted_status_timestamp'],axis=1,inplace=True)
In [84]:
## Dropping retweeted_status_id
twitter_master_clean.drop('retweeted_status_id',axis=1,inplace=True)
In [85]:
#### Testing
twitter_master_clean.columns
Out[85]:
Index(['tweet_id', 'timestamp', 'source', 'text', 'expanded_urls',
'rating_numerator', 'rating_denominator', 'name', 'dog_stages',
'jpg_url', 'img_num', 'p1', 'p1_conf', 'p1_dog', 'p2', 'p2_conf',
'p2_dog', 'p3', 'p3_conf', 'p3_dog', 'favorite_count', 'retweet_count'],
dtype='object')
In [86]:
twitter_master_clean.head()
Out[86]:
| tweet_id | timestamp | source | text | expanded_urls | rating_numerator | rating_denominator | name | dog_stages | jpg_url | ... | p1_conf | p1_dog | p2 | p2_conf | p2_dog | p3 | p3_conf | p3_dog | favorite_count | retweet_count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 892420643555336193 | 2017-08-01 16:23:56 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Phineas. He's a mystical boy. Only eve... | https://twitter.com/dog_rates/status/892420643... | 13 | 10 | Phineas | None | https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg | ... | 0.097049 | False | bagel | 0.085851 | False | banana | 0.076110 | False | 39467 | 8853 |
| 1 | 892177421306343426 | 2017-08-01 00:17:27 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Tilly. She's just checking pup on you.... | https://twitter.com/dog_rates/status/892177421... | 13 | 10 | Tilly | None | https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg | ... | 0.323581 | True | Pekinese | 0.090647 | True | papillon | 0.068957 | True | 33819 | 6514 |
| 2 | 891815181378084864 | 2017-07-31 00:18:03 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Archie. He is a rare Norwegian Pouncin... | https://twitter.com/dog_rates/status/891815181... | 12 | 10 | Archie | None | https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg | ... | 0.716012 | True | malamute | 0.078253 | True | kelpie | 0.031379 | True | 25461 | 4328 |
| 3 | 891689557279858688 | 2017-07-30 15:58:51 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Darla. She commenced a snooze mid meal... | https://twitter.com/dog_rates/status/891689557... | 13 | 10 | Darla | None | https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg | ... | 0.170278 | False | Labrador_retriever | 0.168086 | True | spatula | 0.040836 | False | 42908 | 8964 |
| 4 | 891327558926688256 | 2017-07-29 16:00:24 +0000 | <a href="http://twitter.com/download/iphone" r... | This is Franklin. He would like you to stop ca... | https://twitter.com/dog_rates/status/891327558... | 12 | 10 | Franklin | None | https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg | ... | 0.555712 | True | English_springer | 0.225770 | True | German_short-haired_pointer | 0.175219 | True | 41048 | 9774 |
5 rows × 22 columns
Define¶
- Converting timestamp into date and time format from string
Code¶
In [87]:
twitter_master_clean['timestamp'] = pd.to_datetime(twitter_master_clean['timestamp'])
Testing¶
In [88]:
type(twitter_master_clean['timestamp'][0])
Out[88]:
pandas._libs.tslibs.timestamps.Timestamp
In [89]:
twitter_master_clean.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 2311 entries, 0 to 2405 Data columns (total 22 columns): tweet_id 2311 non-null int64 timestamp 2311 non-null datetime64[ns] source 2311 non-null object text 2311 non-null object expanded_urls 2311 non-null object rating_numerator 2311 non-null int64 rating_denominator 2311 non-null int64 name 2311 non-null object dog_stages 2311 non-null object jpg_url 2311 non-null object img_num 2311 non-null int64 p1 2311 non-null object p1_conf 2311 non-null float64 p1_dog 2311 non-null bool p2 2311 non-null object p2_conf 2311 non-null float64 p2_dog 2311 non-null bool p3 2311 non-null object p3_conf 2311 non-null float64 p3_dog 2311 non-null bool favorite_count 2311 non-null int64 retweet_count 2311 non-null int64 dtypes: bool(3), datetime64[ns](1), float64(3), int64(6), object(9) memory usage: 447.9+ KB
Define¶
- Create the list of incorrect name entries and replace them with 'NG'(Not_Given) and then converting every name into lowercase
Code¶
In [90]:
name_1 = ['his','a','the','an','quite','such','None']
for name in name_1:
twitter_master_clean['name'].replace(name,'NG',inplace=True)
In [91]:
### Converting all the names in lower case
twitter_master_clean['name']=twitter_master_clean.name.str.lower()
Testing¶
In [92]:
twitter_master_clean['name'].head()
Out[92]:
0 phineas 1 tilly 2 archie 3 darla 4 franklin Name: name, dtype: object
In [93]:
twitter_master_clean['name'].tail()
Out[93]:
2401 ng 2402 ng 2403 ng 2404 ng 2405 ng Name: name, dtype: object
In [94]:
twitter_master_clean.name.sample(5)
Out[94]:
1729 hector 1807 ng 2123 clarence 1130 ng 865 brandonald Name: name, dtype: object
Define¶
Replacing the none values in dog_stages np.nan
Code¶
In [95]:
twitter_master_clean.dog_stages.unique()
Out[95]:
array(['None', 'doggo', 'puppo', 'pupper', 'floofer'], dtype=object)
In [96]:
twitter_master_clean.dog_stages.replace('None',np.nan,inplace=True)
Testing¶
In [97]:
twitter_master_clean.dog_stages.value_counts()
Out[97]:
pupper 212 doggo 74 puppo 23 floofer 8 Name: dog_stages, dtype: int64
In [98]:
twitter_master_clean.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 2311 entries, 0 to 2405 Data columns (total 22 columns): tweet_id 2311 non-null int64 timestamp 2311 non-null datetime64[ns] source 2311 non-null object text 2311 non-null object expanded_urls 2311 non-null object rating_numerator 2311 non-null int64 rating_denominator 2311 non-null int64 name 2311 non-null object dog_stages 317 non-null object jpg_url 2311 non-null object img_num 2311 non-null int64 p1 2311 non-null object p1_conf 2311 non-null float64 p1_dog 2311 non-null bool p2 2311 non-null object p2_conf 2311 non-null float64 p2_dog 2311 non-null bool p3 2311 non-null object p3_conf 2311 non-null float64 p3_dog 2311 non-null bool favorite_count 2311 non-null int64 retweet_count 2311 non-null int64 dtypes: bool(3), datetime64[ns](1), float64(3), int64(6), object(9) memory usage: 447.9+ KB
Define¶
- Extract the source from the source url and add it in human readible form
Code¶
In [99]:
twitter_master_clean.source.value_counts()
Out[99]:
<a href="http://twitter.com/download/iphone" rel="nofollow">Twitter for iPhone</a> 2269 <a href="http://twitter.com" rel="nofollow">Twitter Web Client</a> 29 <a href="https://about.twitter.com/products/tweetdeck" rel="nofollow">TweetDeck</a> 13 Name: source, dtype: int64
In [100]:
twitter_master_clean['source']=twitter_master_clean.source.apply(lambda i : re.findall(r'>(.*)<',i)[0])
Testing¶
In [101]:
twitter_master_clean.source.value_counts()
Out[101]:
Twitter for iPhone 2269 Twitter Web Client 29 TweetDeck 13 Name: source, dtype: int64
Define¶
- Filter the values that does not have denomitor equal to 10 and then check the details in the text column and correct the values in rating_denominator column
Code¶
In [102]:
twitter_master_clean[twitter_master_clean.rating_denominator != 10]
Out[102]:
| tweet_id | timestamp | source | text | expanded_urls | rating_numerator | rating_denominator | name | dog_stages | jpg_url | ... | p1_conf | p1_dog | p2 | p2_conf | p2_dog | p3 | p3_conf | p3_dog | favorite_count | retweet_count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 407 | 820690176645140481 | 2017-01-15 17:52:40 | Twitter for iPhone | The floofs have been released I repeat the flo... | https://twitter.com/dog_rates/status/820690176... | 84 | 70 | ng | NaN | https://pbs.twimg.com/media/C2OtWr0VQAEnS9r.jpg | ... | 0.872064 | True | kuvasz | 0.059526 | True | Samoyed | 0.037400 | True | 13518 | 3716 |
| 495 | 810984652412424192 | 2016-12-19 23:06:23 | Twitter for iPhone | Meet Sam. She smiles 24/7 & secretly aspir... | https://www.gofundme.com/sams-smile,https://tw... | 24 | 7 | sam | NaN | https://pbs.twimg.com/media/C0EyPZbXAAAceSc.jpg | ... | 0.871342 | True | Tibetan_mastiff | 0.036708 | True | Labrador_retriever | 0.025823 | True | 5927 | 1655 |
| 866 | 758467244762497024 | 2016-07-28 01:00:57 | Twitter for iPhone | Why does this never happen at my front door...... | https://twitter.com/dog_rates/status/758467244... | 165 | 150 | ng | NaN | https://pbs.twimg.com/ext_tw_video_thumb/75846... | ... | 0.436377 | True | Chihuahua | 0.113956 | True | American_Staffordshire_terrier | 0.099689 | True | 5316 | 2539 |
| 1044 | 740373189193256964 | 2016-06-08 02:41:38 | Twitter for iPhone | After so many requests, this is Bretagne. She ... | https://twitter.com/dog_rates/status/740373189... | 9 | 11 | ng | NaN | https://pbs.twimg.com/media/CkZVdJ6WYAAXZ5A.jpg | ... | 0.807644 | True | kuvasz | 0.101286 | True | Labrador_retriever | 0.023785 | True | 20648 | 9220 |
| 1104 | 731156023742988288 | 2016-05-13 16:15:54 | Twitter for iPhone | Say hello to this unbelievably well behaved sq... | https://twitter.com/dog_rates/status/731156023... | 204 | 170 | this | NaN | https://pbs.twimg.com/media/CiWWhVNUYAAab_r.jpg | ... | 0.501767 | False | breakwater | 0.051351 | False | king_penguin | 0.049444 | False | 4196 | 1434 |
| 1157 | 722974582966214656 | 2016-04-21 02:25:47 | Twitter for iPhone | Happy 4/20 from the squad! 13/10 for all https... | https://twitter.com/dog_rates/status/722974582... | 4 | 20 | ng | NaN | https://pbs.twimg.com/media/CgiFjIpWgAA4wVp.jpg | ... | 0.246762 | True | Greater_Swiss_Mountain_dog | 0.126131 | True | Weimaraner | 0.085297 | True | 4493 | 1764 |
| 1197 | 716439118184652801 | 2016-04-03 01:36:11 | Twitter for iPhone | This is Bluebert. He just saw that both #Final... | https://twitter.com/dog_rates/status/716439118... | 50 | 50 | bluebert | NaN | https://pbs.twimg.com/media/CfFNk7cWAAA-hND.jpg | ... | 0.396495 | True | malamute | 0.317053 | True | Eskimo_dog | 0.273419 | True | 2574 | 247 |
| 1219 | 713900603437621249 | 2016-03-27 01:29:02 | Twitter for iPhone | Happy Saturday here's 9 puppers on a bench. 99... | https://twitter.com/dog_rates/status/713900603... | 99 | 90 | ng | NaN | https://pbs.twimg.com/media/CehIzzZWQAEyHH5.jpg | ... | 0.371816 | True | cocker_spaniel | 0.177413 | True | Irish_setter | 0.092725 | True | 3062 | 829 |
| 1246 | 710658690886586372 | 2016-03-18 02:46:49 | Twitter for iPhone | Here's a brigade of puppers. All look very pre... | https://twitter.com/dog_rates/status/710658690... | 80 | 80 | ng | NaN | https://pbs.twimg.com/media/CdzETn4W4AAVU5N.jpg | ... | 0.948617 | True | Dandie_Dinmont | 0.018664 | True | cairn | 0.015943 | True | 2529 | 636 |
| 1265 | 709198395643068416 | 2016-03-14 02:04:08 | Twitter for iPhone | From left to right:\nCletus, Jerome, Alejandro... | https://twitter.com/dog_rates/status/709198395... | 45 | 50 | ng | NaN | https://pbs.twimg.com/media/CdeUKpcWoAAJAWJ.jpg | ... | 0.490783 | True | wire-haired_fox_terrier | 0.083513 | True | English_setter | 0.083184 | True | 2634 | 721 |
| 1341 | 704054845121142784 | 2016-02-28 21:25:30 | Twitter for iPhone | Here is a whole flock of puppers. 60/50 I'll ... | https://twitter.com/dog_rates/status/704054845... | 60 | 50 | ng | NaN | https://pbs.twimg.com/media/CcVOJEcXEAM0FHL.jpg | ... | 0.667939 | True | kuvasz | 0.228764 | True | golden_retriever | 0.043885 | True | 3201 | 1028 |
| 1430 | 697463031882764288 | 2016-02-10 16:51:59 | Twitter for iPhone | Happy Wednesday here's a bucket of pups. 44/40... | https://twitter.com/dog_rates/status/697463031... | 44 | 40 | ng | NaN | https://pbs.twimg.com/media/Ca3i7CzXIAMLhg8.jpg | ... | 0.999885 | True | golden_retriever | 0.000098 | True | pug | 0.000008 | True | 3748 | 1552 |
| 1641 | 684225744407494656 | 2016-01-05 04:11:44 | Twitter for iPhone | Two sneaky puppers were not initially seen, mo... | https://twitter.com/dog_rates/status/684225744... | 143 | 130 | ng | NaN | https://pbs.twimg.com/media/CX7br3HWsAAQ9L1.jpg | ... | 0.203249 | True | Samoyed | 0.067958 | True | Great_Pyrenees | 0.065327 | True | 1369 | 239 |
| 1642 | 684222868335505415 | 2016-01-05 04:00:18 | Twitter for iPhone | Someone help the girl is being mugged. Several... | https://twitter.com/dog_rates/status/684222868... | 121 | 110 | ng | NaN | https://pbs.twimg.com/media/CX7Y_ByWwAEJdUy.jpg | ... | 0.791182 | True | cocker_spaniel | 0.072444 | True | teddy | 0.071486 | False | 4225 | 1563 |
| 1674 | 682962037429899265 | 2016-01-01 16:30:13 | Twitter for iPhone | This is Darrel. He just robbed a 7/11 and is i... | https://twitter.com/dog_rates/status/682962037... | 7 | 11 | darrel | NaN | https://pbs.twimg.com/media/CXpeVzQW8AApKYb.jpg | ... | 0.278600 | False | Chihuahua | 0.155207 | True | loupe | 0.153598 | False | 26239 | 15043 |
| 1802 | 677716515794329600 | 2015-12-18 05:06:23 | Twitter for iPhone | IT'S PUPPERGEDDON. Total of 144/120 ...I think... | https://twitter.com/dog_rates/status/677716515... | 144 | 120 | ng | NaN | https://pbs.twimg.com/media/CWe7kw9W4AE8UJh.jpg | ... | 0.662908 | False | crib | 0.031891 | False | chow | 0.025438 | True | 3323 | 1104 |
| 1871 | 675853064436391936 | 2015-12-13 01:41:41 | Twitter for iPhone | Here we have an entire platoon of puppers. Tot... | https://twitter.com/dog_rates/status/675853064... | 88 | 80 | ng | NaN | https://pbs.twimg.com/media/CWEcxqWVEAAHyGH.jpg | ... | 0.868367 | True | golden_retriever | 0.043305 | True | vizsla | 0.028207 | True | 2927 | 1460 |
| 2385 | 666287406224695296 | 2015-11-16 16:11:11 | Twitter for iPhone | This is an Albanian 3 1/2 legged Episcopalian... | https://twitter.com/dog_rates/status/666287406... | 1 | 2 | ng | NaN | https://pbs.twimg.com/media/CT8g3BpUEAAuFjg.jpg | ... | 0.857531 | True | toy_poodle | 0.063064 | True | miniature_poodle | 0.025581 | True | 152 | 71 |
18 rows × 22 columns
In [103]:
d= twitter_master_clean.copy()
In [104]:
for n in twitter_master_clean['rating_denominator']:
if n > 10 or n < 10:
twitter_master_clean['rating_denominator'].replace(n,10,inplace=True)
else:
twitter_master_clean['rating_denominator']
Testing¶
In [105]:
twitter_master_clean[twitter_master_clean.rating_denominator != 10]
Out[105]:
| tweet_id | timestamp | source | text | expanded_urls | rating_numerator | rating_denominator | name | dog_stages | jpg_url | ... | p1_conf | p1_dog | p2 | p2_conf | p2_dog | p3 | p3_conf | p3_dog | favorite_count | retweet_count |
|---|
0 rows × 22 columns
In [106]:
twitter_master_clean.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 2311 entries, 0 to 2405 Data columns (total 22 columns): tweet_id 2311 non-null int64 timestamp 2311 non-null datetime64[ns] source 2311 non-null object text 2311 non-null object expanded_urls 2311 non-null object rating_numerator 2311 non-null int64 rating_denominator 2311 non-null int64 name 2311 non-null object dog_stages 317 non-null object jpg_url 2311 non-null object img_num 2311 non-null int64 p1 2311 non-null object p1_conf 2311 non-null float64 p1_dog 2311 non-null bool p2 2311 non-null object p2_conf 2311 non-null float64 p2_dog 2311 non-null bool p3 2311 non-null object p3_conf 2311 non-null float64 p3_dog 2311 non-null bool favorite_count 2311 non-null int64 retweet_count 2311 non-null int64 dtypes: bool(3), datetime64[ns](1), float64(3), int64(6), object(9) memory usage: 447.9+ KB
In [107]:
### Checking if the shape of twitter_master_clean values ==10 has same number of row values
twitter_master_clean[twitter_master_clean.rating_denominator == 10].shape[0]
Out[107]:
2311
Define¶
Change the type of tweet_id from int64 to string because the id's should be string and we won't be using it for analysis
Code¶
In [108]:
type(twitter_master_clean.tweet_id[0])
Out[108]:
numpy.int64
In [109]:
twitter_master_clean.tweet_id = twitter_master_clean.tweet_id.astype('str')
Testing¶
In [110]:
type(twitter_master_clean.tweet_id[0])
Out[110]:
str
Define¶
Change the names in p1, p2, p3, in lower case
Code¶
In [111]:
s=['p1','p2','p3']
for n in s:
twitter_master_clean[n]=twitter_master_clean[n].str.lower()
Testing¶
In [112]:
twitter_master_clean.p1.head()
Out[112]:
0 orange 1 chihuahua 2 chihuahua 3 paper_towel 4 basset Name: p1, dtype: object
In [113]:
twitter_master_clean.p2.head()
Out[113]:
0 bagel 1 pekinese 2 malamute 3 labrador_retriever 4 english_springer Name: p2, dtype: object
In [114]:
twitter_master_clean.p3.head()
Out[114]:
0 banana 1 papillon 2 kelpie 3 spatula 4 german_short-haired_pointer Name: p3, dtype: object
Define¶
Change the names of p1, p1_conf,p1_dog,p2_conf,p2_dog,p3,p3_conf,p3_dog
Code¶
In [115]:
twitter_master_clean.rename(columns={'p1':'first_predict_breed','p1_conf':'prediction1_confidence','p1_dog':'predict1_result'
,'p2':'second_predict_breed','p2_conf':'prediction2_confidence','p2_dog':'predict2_result',
'p3':'third_predict_breed','p3_conf':'prediction3_confidence','p3_dog':'predict3_result'}
,inplace=True)
Testing¶
In [116]:
twitter_master_clean.columns
Out[116]:
Index(['tweet_id', 'timestamp', 'source', 'text', 'expanded_urls',
'rating_numerator', 'rating_denominator', 'name', 'dog_stages',
'jpg_url', 'img_num', 'first_predict_breed', 'prediction1_confidence',
'predict1_result', 'second_predict_breed', 'prediction2_confidence',
'predict2_result', 'third_predict_breed', 'prediction3_confidence',
'predict3_result', 'favorite_count', 'retweet_count'],
dtype='object')
Define¶
Extract month,day,year from timestamp column
Code¶
In [117]:
### Extracting year
twitter_master_clean['year'] = twitter_master_clean['timestamp'].dt.year
In [118]:
### Extracting month
twitter_master_clean['month'] = twitter_master_clean['timestamp'].dt.month
In [119]:
### Extracting day
twitter_master_clean['day'] = twitter_master_clean['timestamp'].dt.day
In [120]:
### Extracting time
twitter_master_clean['time'] = twitter_master_clean['timestamp'].dt.time
In [121]:
### Extracting day_of_week
twitter_master_clean['day_of_week'] = twitter_master_clean['timestamp'].dt.dayofweek
days = {0:'Mon',1:'Tues',2:'Weds',3:'Thurs',4:'Fri',5:'Sat',6:'Sun'}
twitter_master_clean['day_of_week'] = twitter_master_clean['day_of_week'].apply(lambda x: days[x])
Testing¶
In [122]:
twitter_master_clean[['year','month','day','time','day_of_week']].head()
Out[122]:
| year | month | day | time | day_of_week | |
|---|---|---|---|---|---|
| 0 | 2017 | 8 | 1 | 16:23:56 | Tues |
| 1 | 2017 | 8 | 1 | 00:17:27 | Tues |
| 2 | 2017 | 7 | 31 | 00:18:03 | Mon |
| 3 | 2017 | 7 | 30 | 15:58:51 | Sun |
| 4 | 2017 | 7 | 29 | 16:00:24 | Sat |
Storing¶
In [123]:
#### Writing the cleaned twitter_master_clean into a csvfile
twitter_master_clean.to_csv('twitter_archive_master.csv',encoding='utf-8',index=False)
Analysis¶
In [124]:
### Reading the master file
twitter_archive_master = pd.read_csv("twitter_archive_master.csv")
In [125]:
twitter_archive_master.head()
Out[125]:
| tweet_id | timestamp | source | text | expanded_urls | rating_numerator | rating_denominator | name | dog_stages | jpg_url | ... | third_predict_breed | prediction3_confidence | predict3_result | favorite_count | retweet_count | year | month | day | time | day_of_week | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 892420643555336193 | 2017-08-01 16:23:56 | Twitter for iPhone | This is Phineas. He's a mystical boy. Only eve... | https://twitter.com/dog_rates/status/892420643... | 13 | 10 | phineas | NaN | https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg | ... | banana | 0.076110 | False | 39467 | 8853 | 2017 | 8 | 1 | 16:23:56 | Tues |
| 1 | 892177421306343426 | 2017-08-01 00:17:27 | Twitter for iPhone | This is Tilly. She's just checking pup on you.... | https://twitter.com/dog_rates/status/892177421... | 13 | 10 | tilly | NaN | https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg | ... | papillon | 0.068957 | True | 33819 | 6514 | 2017 | 8 | 1 | 00:17:27 | Tues |
| 2 | 891815181378084864 | 2017-07-31 00:18:03 | Twitter for iPhone | This is Archie. He is a rare Norwegian Pouncin... | https://twitter.com/dog_rates/status/891815181... | 12 | 10 | archie | NaN | https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg | ... | kelpie | 0.031379 | True | 25461 | 4328 | 2017 | 7 | 31 | 00:18:03 | Mon |
| 3 | 891689557279858688 | 2017-07-30 15:58:51 | Twitter for iPhone | This is Darla. She commenced a snooze mid meal... | https://twitter.com/dog_rates/status/891689557... | 13 | 10 | darla | NaN | https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg | ... | spatula | 0.040836 | False | 42908 | 8964 | 2017 | 7 | 30 | 15:58:51 | Sun |
| 4 | 891327558926688256 | 2017-07-29 16:00:24 | Twitter for iPhone | This is Franklin. He would like you to stop ca... | https://twitter.com/dog_rates/status/891327558... | 12 | 10 | franklin | NaN | https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg | ... | german_short-haired_pointer | 0.175219 | True | 41048 | 9774 | 2017 | 7 | 29 | 16:00:24 | Sat |
5 rows × 27 columns
In [126]:
### Analyze the source for sending the twitter feeds using countplot
In [127]:
sort=twitter_archive_master['source'].value_counts().index
In [128]:
colours = ["maroon", "navy", "gold"]
sns.set(style='whitegrid')
sns.countplot(data=twitter_archive_master,y='source',order=sort,palette=colours)
plt.xticks(rotation=360)
plt.xlabel("Count",fontsize=12)
plt.ylabel("Source",fontsize=12)
plt.title("SourceCount",fontsize=17)
plt.show()
Here we can see the most number of twitter updates are done using i-phone app and then people use the Twitter web_client
In [129]:
## Filtter the numerator that has values less than 15
In [130]:
d2= twitter_archive_master[twitter_archive_master['rating_numerator'] <=15]
In [131]:
d2.head()
Out[131]:
| tweet_id | timestamp | source | text | expanded_urls | rating_numerator | rating_denominator | name | dog_stages | jpg_url | ... | third_predict_breed | prediction3_confidence | predict3_result | favorite_count | retweet_count | year | month | day | time | day_of_week | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 892420643555336193 | 2017-08-01 16:23:56 | Twitter for iPhone | This is Phineas. He's a mystical boy. Only eve... | https://twitter.com/dog_rates/status/892420643... | 13 | 10 | phineas | NaN | https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg | ... | banana | 0.076110 | False | 39467 | 8853 | 2017 | 8 | 1 | 16:23:56 | Tues |
| 1 | 892177421306343426 | 2017-08-01 00:17:27 | Twitter for iPhone | This is Tilly. She's just checking pup on you.... | https://twitter.com/dog_rates/status/892177421... | 13 | 10 | tilly | NaN | https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg | ... | papillon | 0.068957 | True | 33819 | 6514 | 2017 | 8 | 1 | 00:17:27 | Tues |
| 2 | 891815181378084864 | 2017-07-31 00:18:03 | Twitter for iPhone | This is Archie. He is a rare Norwegian Pouncin... | https://twitter.com/dog_rates/status/891815181... | 12 | 10 | archie | NaN | https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg | ... | kelpie | 0.031379 | True | 25461 | 4328 | 2017 | 7 | 31 | 00:18:03 | Mon |
| 3 | 891689557279858688 | 2017-07-30 15:58:51 | Twitter for iPhone | This is Darla. She commenced a snooze mid meal... | https://twitter.com/dog_rates/status/891689557... | 13 | 10 | darla | NaN | https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg | ... | spatula | 0.040836 | False | 42908 | 8964 | 2017 | 7 | 30 | 15:58:51 | Sun |
| 4 | 891327558926688256 | 2017-07-29 16:00:24 | Twitter for iPhone | This is Franklin. He would like you to stop ca... | https://twitter.com/dog_rates/status/891327558... | 12 | 10 | franklin | NaN | https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg | ... | german_short-haired_pointer | 0.175219 | True | 41048 | 9774 | 2017 | 7 | 29 | 16:00:24 | Sat |
5 rows × 27 columns
In [132]:
### Calculating the ratio of popuarity
In [133]:
d2['popularity']=d2['rating_numerator']/d2['rating_denominator']
/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy """Entry point for launching an IPython kernel.
In [134]:
d2.head()
Out[134]:
| tweet_id | timestamp | source | text | expanded_urls | rating_numerator | rating_denominator | name | dog_stages | jpg_url | ... | prediction3_confidence | predict3_result | favorite_count | retweet_count | year | month | day | time | day_of_week | popularity | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 892420643555336193 | 2017-08-01 16:23:56 | Twitter for iPhone | This is Phineas. He's a mystical boy. Only eve... | https://twitter.com/dog_rates/status/892420643... | 13 | 10 | phineas | NaN | https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg | ... | 0.076110 | False | 39467 | 8853 | 2017 | 8 | 1 | 16:23:56 | Tues | 1.3 |
| 1 | 892177421306343426 | 2017-08-01 00:17:27 | Twitter for iPhone | This is Tilly. She's just checking pup on you.... | https://twitter.com/dog_rates/status/892177421... | 13 | 10 | tilly | NaN | https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg | ... | 0.068957 | True | 33819 | 6514 | 2017 | 8 | 1 | 00:17:27 | Tues | 1.3 |
| 2 | 891815181378084864 | 2017-07-31 00:18:03 | Twitter for iPhone | This is Archie. He is a rare Norwegian Pouncin... | https://twitter.com/dog_rates/status/891815181... | 12 | 10 | archie | NaN | https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg | ... | 0.031379 | True | 25461 | 4328 | 2017 | 7 | 31 | 00:18:03 | Mon | 1.2 |
| 3 | 891689557279858688 | 2017-07-30 15:58:51 | Twitter for iPhone | This is Darla. She commenced a snooze mid meal... | https://twitter.com/dog_rates/status/891689557... | 13 | 10 | darla | NaN | https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg | ... | 0.040836 | False | 42908 | 8964 | 2017 | 7 | 30 | 15:58:51 | Sun | 1.3 |
| 4 | 891327558926688256 | 2017-07-29 16:00:24 | Twitter for iPhone | This is Franklin. He would like you to stop ca... | https://twitter.com/dog_rates/status/891327558... | 12 | 10 | franklin | NaN | https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg | ... | 0.175219 | True | 41048 | 9774 | 2017 | 7 | 29 | 16:00:24 | Sat | 1.2 |
5 rows × 28 columns
In [135]:
### Analyzing the most famous dog breed in firstprediction
group1 = d2.groupby(['first_predict_breed'],as_index=False).agg({'popularity' :['max']})
In [136]:
d2.columns
Out[136]:
Index(['tweet_id', 'timestamp', 'source', 'text', 'expanded_urls',
'rating_numerator', 'rating_denominator', 'name', 'dog_stages',
'jpg_url', 'img_num', 'first_predict_breed', 'prediction1_confidence',
'predict1_result', 'second_predict_breed', 'prediction2_confidence',
'predict2_result', 'third_predict_breed', 'prediction3_confidence',
'predict3_result', 'favorite_count', 'retweet_count', 'year', 'month',
'day', 'time', 'day_of_week', 'popularity'],
dtype='object')
In [137]:
group1.columns =group1.columns.droplevel(level=1)
In [138]:
group1.sort_values('popularity',ascending=False,inplace=True)
In [139]:
group1.replace('laptop','lapdog',inplace=True)
In [140]:
a = group1.sample(20)
In [141]:
a.sort_values('popularity',ascending=False,inplace=True)
In [142]:
plt.figure(figsize=[20.5,13.5])
sns.barplot(data=a,x='first_predict_breed',y='popularity')
plt.xticks(rotation=45)
plt.xlabel('Type1Breed')
plt.ylabel('Popularity')
plt.title('Type1_Popularity')
Out[142]:
Text(0.5,1,'Type1_Popularity')
The graph above shows the graph of the sample rating from this we can know that rating range's as high as 1.3 to as low apprx 0.2 The values will change as this is based on sample data just to show case the rating trend of the dogs
In [143]:
### Analyzing the number of retweet_count and favourite_count based upon year
plt.figure(figsize=[20.5,13.5])
sns.scatterplot(data=twitter_archive_master,x='favorite_count',y='retweet_count',hue='year')
plt.xlabel('Favourite_count',fontsize=12)
plt.ylabel('Retweet_count',fontsize=12)
plt.title('Favourite Vs Retweet_count',fontsize=17)
plt.show()
Here we can see relationship between facourite_count and retweet_Count. The relationship is positive. We can also see that most of the values are for all the years are 20000. The maximum value for retweets is 80,000 and maximum value for favourite_count is around 130000
In [144]:
### Analyzing by month
group3= twitter_archive_master.groupby(by=['year','month'],as_index=False).agg({'tweet_id': ['count']})
In [145]:
group3.columns =group3.columns.droplevel(level=1)
In [146]:
group3
Out[146]:
| year | month | tweet_id | |
|---|---|---|---|
| 0 | 2015 | 11 | 296 |
| 1 | 2015 | 12 | 430 |
| 2 | 2016 | 1 | 210 |
| 3 | 2016 | 2 | 131 |
| 4 | 2016 | 3 | 132 |
| 5 | 2016 | 4 | 65 |
| 6 | 2016 | 5 | 72 |
| 7 | 2016 | 6 | 96 |
| 8 | 2016 | 7 | 114 |
| 9 | 2016 | 8 | 63 |
| 10 | 2016 | 9 | 75 |
| 11 | 2016 | 10 | 73 |
| 12 | 2016 | 11 | 68 |
| 13 | 2016 | 12 | 64 |
| 14 | 2017 | 1 | 83 |
| 15 | 2017 | 2 | 79 |
| 16 | 2017 | 3 | 54 |
| 17 | 2017 | 4 | 47 |
| 18 | 2017 | 5 | 48 |
| 19 | 2017 | 6 | 52 |
| 20 | 2017 | 7 | 57 |
| 21 | 2017 | 8 | 2 |
In [147]:
plt.figure(figsize=[20.5,13.5])
sns.relplot(data=group3,x='month',y='tweet_id',kind='line',col='year')
Out[147]:
<seaborn.axisgrid.FacetGrid at 0x7f95b1548b38>
<matplotlib.figure.Figure at 0x7f95b1571da0>
Here we can see the trend of the line graph with counts and the month. We can see that in latter part of 2015 the user count tweeting started increasing and then we can see that in 2016 user count decreased and then in may it started increasing and slowing decreasing again. In 2017 we can see the users that tweeting is reducing
In [148]:
twitter_archive_master.columns
Out[148]:
Index(['tweet_id', 'timestamp', 'source', 'text', 'expanded_urls',
'rating_numerator', 'rating_denominator', 'name', 'dog_stages',
'jpg_url', 'img_num', 'first_predict_breed', 'prediction1_confidence',
'predict1_result', 'second_predict_breed', 'prediction2_confidence',
'predict2_result', 'third_predict_breed', 'prediction3_confidence',
'predict3_result', 'favorite_count', 'retweet_count', 'year', 'month',
'day', 'time', 'day_of_week'],
dtype='object')
In [149]:
sot1=twitter_archive_master['dog_stages'].value_counts()
In [151]:
sot1
Out[151]:
pupper 212 doggo 74 puppo 23 floofer 8 Name: dog_stages, dtype: int64
In [156]:
colours = ["blue", "green", "yellow"]
sns.set(style='whitegrid')
sns.countplot(data=twitter_archive_master,x='dog_stages',order=sot1.index,palette=colours)
plt.xticks(rotation=45)
plt.xlabel("Count",fontsize=12)
plt.ylabel("Dog_Stage",fontsize=12)
plt.title("Dog_StageDistribution",fontsize=17)
plt.show()
Here,we can see the distribution of dog stages. It shows that ‘pupper’ (a small doggo, usually younger) is the most popular dog stage, followed by ‘doggo’ and ‘puppo’. It could be due to the young and unmatured dog is usually cuter than the adult dog. It should also be noticed that there’s huge amount missing data in dog stage, hence the distrubition might not be true.
Image prediction Data Analysis¶
In [166]:
sns.set(style='darkgrid')
sns.set(color_codes=True)
sns.distplot(twitter_archive_master['prediction1_confidence'],color='green')
plt.xlabel('Confidencelevel')
plt.ylabel('Frequency')
plt.title('Distrubutionplot')
Out[166]:
Text(0.5,1,'Distrubutionplot')
In [163]:
sort1=twitter_archive_master['predict1_result'].value_counts()
In [164]:
sort1
Out[164]:
True 1715 False 596 Name: predict1_result, dtype: int64
In [165]:
explode = (0.1,0)
fig1,ax1=plt.subplots()
ax1.pie(sort1,explode=explode,labels=sort1.index,autopct='%1.1f%%',shadow=True,startangle=60)
ax1.axis('equal')
plt.tight_layout()
plt.show()
The first plot above shows the prediction success rate of whether or not first prediction is a breed of dog. The pie chart indicates almost 74.2% situations the predictions are correct, even though this result is not good enough for a deep learning model. The second plot shows how confident the algorithm is in its first prediction. We can see 100% is the most cases, however the amounts of 0.1 to 0.8 dominate the entire distribution. That also could suggest that the model is not good enough
Refrences¶
- Youtube [https://www.youtube.com/watch?v=g_Y5NlDBML4]
- Seaborn Tutorian [http://alanpryorjr.com/visualizations/seaborn/countplot/countplot/]
- Class notes
- Twitter API [https://www.slickremix.com/docs/how-to-get-api-keys-and-tokens-for-twitter/]
In [ ]: